From ccb2f296803c1436d614fe6542dd3309152cf41b Mon Sep 17 00:00:00 2001 From: joshcarp Date: Tue, 25 Feb 2025 13:19:32 -0500 Subject: [PATCH 01/13] Migrate to uv, fix dep problems --- .github/workflows/blackisort.yml | 24 +- .github/workflows/pythonlint.yml | 7 +- .github/workflows/pythontests.yml | 9 +- .uvrc | 9 + UV_MIGRATION.md | 55 + pyproject.toml | 72 + setup.py | 8 +- src/human_aware_rl/rllib/rllib.py | 7 +- src/overcooked_demo/server/app.py | 3 +- src/overcooked_demo/server/config.json | 40 +- uv.lock | 3174 ++++++++++++++++++++++++ 11 files changed, 3358 insertions(+), 50 deletions(-) create mode 100644 .uvrc create mode 100644 UV_MIGRATION.md create mode 100644 pyproject.toml create mode 100644 uv.lock diff --git a/.github/workflows/blackisort.yml b/.github/workflows/blackisort.yml index aca4a853..c0d837d6 100644 --- a/.github/workflows/blackisort.yml +++ b/.github/workflows/blackisort.yml @@ -8,15 +8,19 @@ jobs: steps: - uses: actions/checkout@v3 - uses: actions/setup-python@v4 - # https://black.readthedocs.io/en/stable/integrations/github_actions.html - - name: running Black - uses: psf/black@stable with: - options: "--check --diff --line-length 79" - version: "23.3.0" - # https://pycqa.github.io/isort/docs/configuration/github_action.html - - name: running isort - uses: jamescurtin/isort-action@master + python-version: 3.8 + - name: Install uv + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + echo "$HOME/.cargo/bin" >> $GITHUB_PATH + - name: Install black and isort + run: | + uv pip install black==23.3.0 isort + - name: Running Black + run: | + black --check --diff --line-length 79 . + - name: Running isort if: always() - with: - configuration: "--check-only --line-length 79 --profile black" + run: | + isort --check-only --line-length 79 --profile black . diff --git a/.github/workflows/pythonlint.yml b/.github/workflows/pythonlint.yml index 5449425b..18979055 100644 --- a/.github/workflows/pythonlint.yml +++ b/.github/workflows/pythonlint.yml @@ -13,10 +13,13 @@ jobs: with: python-version: 3.8 architecture: 'x64' + - name: Install uv + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + echo "$HOME/.cargo/bin" >> $GITHUB_PATH - name: Install dependencies run: | - python -m pip install --upgrade pip - pip install flake8 + uv pip install flake8 - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names diff --git a/.github/workflows/pythontests.yml b/.github/workflows/pythontests.yml index 08c6cde3..b004754a 100644 --- a/.github/workflows/pythontests.yml +++ b/.github/workflows/pythontests.yml @@ -26,12 +26,15 @@ jobs: with: python-version: 3.8 architecture: 'x64' + - name: Install uv + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + echo "$HOME/.cargo/bin" >> $GITHUB_PATH - name: Install dependencies run: | - python -m pip install --upgrade pip - pip install coverage + uv pip install coverage - name: Setup # needs -e flag for coverage tests to work - run: pip install -e .[harl] + run: uv pip install -e ".[harl]" - name: Run tests and generate coverage report run: | coverage run -m unittest discover -s testing/ -p "*_test.py" diff --git a/.uvrc b/.uvrc new file mode 100644 index 00000000..a02efb25 --- /dev/null +++ b/.uvrc @@ -0,0 +1,9 @@ +[application] +default-python = "3.8" +isolated = true + +[venv] +location = ".venv" + +[pypi] +url = "https://pypi.org/simple" \ No newline at end of file diff --git a/UV_MIGRATION.md b/UV_MIGRATION.md new file mode 100644 index 00000000..1d454cc4 --- /dev/null +++ b/UV_MIGRATION.md @@ -0,0 +1,55 @@ +# Migrating to uv Package Manager + +This project has been configured to use [uv](https://github.com/astral-sh/uv), a fast Python package manager. The following guide explains how to set up and use uv with this project. + +## Installing uv + +To install uv, run: + +```sh +curl -LsSf https://astral.sh/uv/install.sh | sh +``` + +This adds uv to your system. You may need to restart your shell or add it to your PATH. + +## Setup for Development + +To set up a development environment: + +```sh +# Clone the repository +git clone https://github.com/HumanCompatibleAI/overcooked_ai.git +cd overcooked_ai + +# Create a virtual environment and install dependencies +uv venv +source .venv/bin/activate # On Windows: .venv\Scripts\activate + +# Install the project in development mode with all dependencies +uv pip install -e ".[harl]" +``` + +## Common uv Commands + +Replace pip commands with uv equivalents: + +| pip | uv | +|-----|-----| +| `pip install package` | `uv pip install package` | +| `pip install -r requirements.txt` | `uv pip install -r requirements.txt` | +| `pip freeze > requirements.txt` | `uv pip freeze > requirements.txt` | + +## Benefits of uv + +- Faster installation speed +- Better dependency resolution +- Improved caching +- Single binary with no dependencies + +## Project Structure + +The project now uses a `pyproject.toml` file for configuration, which is compatible with uv and modern Python packaging standards. The `setup.py` file has been maintained for backward compatibility. + +## CI/CD Integration + +GitHub Actions workflows have been updated to use uv for dependency installation. \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..087cbd8d --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,72 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "overcooked_ai" +version = "1.1.0" +description = "Cooperative multi-agent environment based on Overcooked" +readme = "README.md" +requires-python = ">=3.9" +license = {file = "LICENSE"} +authors = [ + {name = "Micah Carroll", email = "mdc@berkeley.edu"} +] +urls = {homepage = "https://github.com/HumanCompatibleAI/overcooked_ai"} +keywords = ["Overcooked", "AI", "Reinforcement Learning"] +dependencies = [ + "dill", + "numpy<2.0.0", + "scipy", + "tqdm", + "gymnasium", + "ipython", + "pygame", + "ipywidgets", + "opencv-python", + "eventlet>=0.39.0", + "flask", + "flask-socketio", +] + +[project.optional-dependencies] +harl = [ + "wandb", + "GitPython", + "memory_profiler", + "sacred", + "pymongo", + "matplotlib", + "requests", + "seaborn==0.9.0", + "ray[rllib]>=2.5.0", + "protobuf", + "tensorflow>=2.14.0", +] + +[project.scripts] +overcooked-demo-up = "overcooked_demo:start_server" +overcooked-demo-move = "overcooked_demo:move_agent" + +[tool.setuptools] +package-dir = {"" = "src"} +packages = {find = {where = ["src"]}} + +[tool.setuptools.package-data] +"overcooked_ai_py" = [ + "data/layouts/*.layout", + "data/planners/*.py", + "data/human_data/*.pickle", + "data/graphics/*.png", + "data/graphics/*.json", + "data/fonts/*.ttf", +] +"human_aware_rl" = [ + "static/**/*.pickle", + "static/**/*.csv", + "ppo/trained_example/*.pkl", + "ppo/trained_example/*.json", + "ppo/trained_example/*/.is_checkpoint", + "ppo/trained_example/*/.tune_metadata", + "ppo/trained_example/*/checkpoint-500", +] diff --git a/setup.py b/setup.py index 804bf4a4..5a5f0a77 100644 --- a/setup.py +++ b/setup.py @@ -39,7 +39,7 @@ }, install_requires=[ "dill", - "numpy", + "numpy<2.0.0", "scipy", "tqdm", "gymnasium", @@ -47,6 +47,8 @@ "pygame", "ipywidgets", "opencv-python", + "flask", + "flask-socketio", ], # removed overlapping dependencies extras_require={ @@ -59,9 +61,9 @@ "matplotlib", "requests", "seaborn==0.9.0", - "ray[rllib]==2.0.0", + "ray[rllib]>=2.5.0", "protobuf", - "tensorflow==2.10", + "tensorflow>=2.14.0", ] }, entry_points={ diff --git a/src/human_aware_rl/rllib/rllib.py b/src/human_aware_rl/rllib/rllib.py index 3b14168f..22387efe 100644 --- a/src/human_aware_rl/rllib/rllib.py +++ b/src/human_aware_rl/rllib/rllib.py @@ -9,13 +9,16 @@ import gymnasium import numpy as np import ray -from ray.rllib.agents.ppo import PPOTrainer +# Updated import for Ray 2.5+ +from ray.rllib.algorithms.ppo import PPO as PPOTrainer from ray.rllib.algorithms.callbacks import DefaultCallbacks from ray.rllib.env.multi_agent_env import MultiAgentEnv from ray.rllib.models import ModelCatalog from ray.tune.logger import UnifiedLogger from ray.tune.registry import register_env -from ray.tune.result import DEFAULT_RESULTS_DIR + +# Define DEFAULT_RESULTS_DIR since it's no longer available in Ray 2.5+ +DEFAULT_RESULTS_DIR = os.path.join(os.path.expanduser("~"), "ray_results") from human_aware_rl.rllib.utils import ( get_base_ae, diff --git a/src/overcooked_demo/server/app.py b/src/overcooked_demo/server/app.py index 3094f082..e9b9abd4 100644 --- a/src/overcooked_demo/server/app.py +++ b/src/overcooked_demo/server/app.py @@ -34,7 +34,8 @@ ########### # Read in global config -CONF_PATH = os.getenv("CONF_PATH", "config.json") +script_dir = os.path.dirname(os.path.abspath(__file__)) +CONF_PATH = os.getenv("CONF_PATH", os.path.join(script_dir, "config.json")) with open(CONF_PATH, "r") as f: CONFIG = json.load(f) diff --git a/src/overcooked_demo/server/config.json b/src/overcooked_demo/server/config.json index 8802f2f7..4a8a6e1c 100644 --- a/src/overcooked_demo/server/config.json +++ b/src/overcooked_demo/server/config.json @@ -1,37 +1,19 @@ { - "logfile" : "app.log", - "layouts" : ["cramped_room","counter_circuit_o_1order", "cramped_room_tomato", "asymmetric_advantages", "coordination_ring", "forced_coordination", "counter_circuit", "cramped_corridor", "marshmallow_experiment", "long_cook_time", "forced_coordination_tomato", "asymmetric_advantages_tomato", "marshmallow_experiment_coordination", "pipeline", "you_shall_not_pass", "tutorial_3"], + "logfile" : "overcooked_app.log", + "layouts" : ["cramped_room", "asymmetric_advantages", "coordination_ring", "forced_coordination", "counter_circuit"], + "layout_globals" : { + "NUM_PLAYERS": 2, + "PROP_FEATS_ENCODING": false, + "REW_SHAPING_PARAMS": null + }, + "MAX_GAME_LENGTH" : 900, + "AGENT_DIR" : "../../../data/agents", "MAX_GAMES" : 10, - "MAX_GAME_LENGTH" : 120, - "AGENT_DIR" : "./static/assets/agents", "MAX_FPS" : 30, "predefined" : { - 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src/overcooked_demo/server/pyproject.toml | 42 + src/overcooked_demo/server/run_server.sh | 48 + src/overcooked_demo/server/uv.lock | 1881 +++++++++++++++++++++ src/overcooked_demo/up.sh | 63 +- 9 files changed, 2156 insertions(+), 23 deletions(-) create mode 100644 src/overcooked_demo/server/README.md create mode 100644 src/overcooked_demo/server/dev_helper.py create mode 100644 src/overcooked_demo/server/pyproject.toml create mode 100755 src/overcooked_demo/server/run_server.sh create mode 100644 src/overcooked_demo/server/uv.lock diff --git a/src/overcooked_demo/README.md b/src/overcooked_demo/README.md index 7b4b8728..6b09fac4 100644 --- a/src/overcooked_demo/README.md +++ b/src/overcooked_demo/README.md @@ -7,6 +7,9 @@ A web application where humans can play Overcooked with trained AI agents. * [Installation](#installation) * [Usage](#usage) + * [Docker Deployment](#docker-deployment) + * [Local Run with uv](#local-run-with-uv) + * [Command Line](#command-line) * [Dependencies](#dependencies) * [Using Pre-trained Agents](#using-pre-trained-agents) * [Updating](#updating) @@ -17,8 +20,14 @@ A web application where humans can play Overcooked with trained AI agents. Building the server image requires [Docker](https://docs.docker.com/get-docker/) +For local development with uv, you'll need: +- Python 3.7 or later +- [uv](https://github.com/astral-sh/uv) - Install with `pip install uv` + ## Usage +### Docker Deployment + The server can be deployed locally using the driver script included in the repo. To run the production server, use the command ```bash ./up.sh production @@ -36,6 +45,23 @@ In order to kill the production server, run ./down.sh ``` +### Local Run with uv + +To run the server locally using uv without Docker, use: + +```bash +./up.sh local +``` + +This will: +1. Create a virtual environment in the server directory if it doesn't exist +2. Install all dependencies using uv +3. Start the server on port 5000 + +After running this command, navigate to http://localhost:5000 + +### Command Line + You can also start the server via the command line. After installing the `overcooked_ai` via pip, you can start the server by typing ``` diff --git a/src/overcooked_demo/docker-compose.yml b/src/overcooked_demo/docker-compose.yml index 014c6bbd..6cffc420 100644 --- a/src/overcooked_demo/docker-compose.yml +++ b/src/overcooked_demo/docker-compose.yml @@ -1,5 +1,3 @@ -version : '3.7' - services: app: build: diff --git a/src/overcooked_demo/server/Dockerfile b/src/overcooked_demo/server/Dockerfile index 456d45d2..4b2dab5b 100644 --- a/src/overcooked_demo/server/Dockerfile +++ b/src/overcooked_demo/server/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.7-buster +FROM python:3.9-slim ARG BUILD_ENV ARG OVERCOOKED_BRANCH @@ -6,24 +6,36 @@ ARG GRAPHICS WORKDIR /app -# Install non-chai dependencies -COPY ./requirements.txt ./requirements.txt -RUN pip install -r requirements.txt +# Install uv +RUN pip install uv -# Install eventlet production server if production build -RUN if [ "$BUILD_ENV" = "production" ] ; then pip install eventlet ; fi +# Create a virtual environment for uv +RUN uv venv + +# Copy the pyproject.toml for installation +COPY ./pyproject.toml ./pyproject.toml +COPY ./dev_helper.py ./dev_helper.py -# Clone chai code +# Clone chai code for the human_aware_rl module +RUN apt-get update && apt-get install -y git RUN git clone --recursive https://github.com/HumanCompatibleAI/overcooked_ai.git --branch $OVERCOOKED_BRANCH --single-branch /overcooked_ai # Dummy data_dir so things don't break RUN echo "import os; DATA_DIR=os.path.abspath('.')" >> /overcooked_ai/src/human_aware_rl/data_dir.py -# Install chai dependencies -RUN pip install -e '/overcooked_ai[harl]' +# Install server dependencies +RUN . .venv/bin/activate && uv pip install -e . -RUN apt-get -y update -RUN apt-get install -y libgl1-mesa-dev +# Install the cloned overcooked_ai in development mode +RUN . .venv/bin/activate && uv pip install -e /overcooked_ai + +# Install eventlet production server if production build +RUN if [ "$BUILD_ENV" = "production" ] ; then . .venv/bin/activate && uv pip install "eventlet>=0.39.0" ; fi + +# Install needed packages +RUN apt-get update && apt-get install -y \ + libgl1-mesa-dev \ + && rm -rf /var/lib/apt/lists/* # Copy over remaining files COPY ./static ./static @@ -31,13 +43,11 @@ COPY ./*.py ./ COPY ./graphics/$GRAPHICS ./static/js/graphics.js COPY ./config.json ./config.json - - # Set environment variables that will be used by app.py ENV HOST 0.0.0.0 ENV PORT 5000 ENV CONF_PATH config.json -# Do the thing +# Expose port and run the app, using dev_helper to ensure proper Python path EXPOSE 5000 -CMD ["python", "-u", "app.py"] +CMD [".venv/bin/python", "-m", "dev_helper", "app.py"] diff --git a/src/overcooked_demo/server/README.md b/src/overcooked_demo/server/README.md new file mode 100644 index 00000000..c2ed999e --- /dev/null +++ b/src/overcooked_demo/server/README.md @@ -0,0 +1,48 @@ +# Overcooked Demo Server + +This directory contains the server code for the Overcooked AI demo. + +## Running with uv + +The server now uses [uv](https://github.com/astral-sh/uv) for dependency management and running Python code. + +### Prerequisites + +Install uv: + +```bash +pip install uv +``` + +### Running the Server + +You can run the server directly using: + +```bash +uv run app.py +``` + +Alternatively, use the provided shell script: + +```bash +./run_server.sh +``` + +## Docker + +The Dockerfile has been updated to use uv as well. Build and run the Docker container with: + +```bash +# Build the container +docker build -t overcooked-server \ + --build-arg BUILD_ENV=development \ + --build-arg OVERCOOKED_BRANCH=master \ + --build-arg GRAPHICS=js . + +# Run the container +docker run -p 5000:5000 overcooked-server +``` + +## Dependencies + +Dependencies are now managed through the `pyproject.toml` file. If you need to add or modify dependencies, update this file instead of the legacy `requirements.txt`. \ No newline at end of file diff --git a/src/overcooked_demo/server/dev_helper.py b/src/overcooked_demo/server/dev_helper.py new file mode 100644 index 00000000..9b4c086f --- /dev/null +++ b/src/overcooked_demo/server/dev_helper.py @@ -0,0 +1,29 @@ +""" +Helper module to add the parent src directory to the Python path. +This allows importing human_aware_rl and other modules from the main project. + +Usage: + # At the top of your script (e.g., app.py) + import os + import sys + + # Add parent directory to path + sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) + + # Now you can import from human_aware_rl + from human_aware_rl.rllib.rllib import load_agent +""" + +import os +import sys +import pathlib + +def add_src_to_path(): + """Add the parent src directory to the Python path.""" + current_dir = pathlib.Path(__file__).parent.absolute() + src_dir = current_dir.parent.parent # ../.. relative to this file + sys.path.insert(0, str(src_dir)) + print(f"Added {src_dir} to Python path") + +# Automatically add to path when imported +add_src_to_path() \ No newline at end of file diff --git a/src/overcooked_demo/server/pyproject.toml b/src/overcooked_demo/server/pyproject.toml new file mode 100644 index 00000000..0fa4df9e --- /dev/null +++ b/src/overcooked_demo/server/pyproject.toml @@ -0,0 +1,42 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "overcooked_server" +version = "1.0.0" +description = "Server for Overcooked AI demo" +requires-python = ">=3.9" +dependencies = [ + "dill", + "numpy<2.0.0", + "certifi>=2020.6.20", + "click>=8.0", + "dnspython>=1.16.0", + "Flask>=2.1.0", + "Flask-SocketIO>=4.3.0", + "greenlet>=0.4.16", + "itsdangerous>=2.0", + "Jinja2>=3.1.0", + "MarkupSafe>=2.0", + "monotonic>=1.5", + "python-engineio>=3.13.0", + "python-socketio>=4.6.0", + "six>=1.15.0", + "Werkzeug>=2.0.3", + "requests>=2.23.0", + "protobuf>=3.19", + "eventlet>=0.39.0", + "ray[rllib]>=2.5.0", +] + +[project.optional-dependencies] +tensorflow = [ + "tensorflow>=2.14.0", +] + +[tool.setuptools] +py-modules = [] + +[tool.setuptools.package-dir] +"" = "../.." \ No newline at end of file diff --git a/src/overcooked_demo/server/run_server.sh b/src/overcooked_demo/server/run_server.sh new file mode 100755 index 00000000..d79b2891 --- /dev/null +++ b/src/overcooked_demo/server/run_server.sh @@ -0,0 +1,48 @@ +#!/bin/bash + +# This script runs the Overcooked server using uv + +# Check if uv is installed +if ! command -v uv &> /dev/null +then + echo "uv could not be found. Please install it first using 'pip install uv'" + exit 1 +fi + +# Create a venv if it doesn't exist +if [ ! -d ".venv" ]; then + echo "Creating virtual environment..." + uv venv +fi + +# Activate the virtual environment +source .venv/bin/activate + +# Install dependencies if needed +if [ ! -f ".venv/.initialized" ]; then + # Install dependencies from pyproject.toml + echo "Installing dependencies from pyproject.toml..." + uv pip install -e . + + # Also install root package + echo "Ensuring overcooked_ai is installed..." + ROOT_DIR="$(realpath "$(dirname "$0")/../..")" + uv pip install -e "$ROOT_DIR" + + # Try to install tensorflow with a fallback + echo "Attempting to install tensorflow..." + if ! uv pip install -e ".[tensorflow]" 2>/dev/null; then + echo "Could not install latest tensorflow. Trying compatible version..." + # Try a version known to work on many platforms + if ! uv pip install "tensorflow>=2.8.0" 2>/dev/null; then + echo "Warning: Could not install tensorflow. The server will run with limited functionality." + fi + fi + + # Mark as initialized + touch .venv/.initialized +fi + +# Run the server using the Python interpreter from the venv +echo "Starting server..." +.venv/bin/python -m dev_helper app.py "$@" \ No newline at end of file diff --git a/src/overcooked_demo/server/uv.lock b/src/overcooked_demo/server/uv.lock new file mode 100644 index 00000000..2561e090 --- /dev/null +++ b/src/overcooked_demo/server/uv.lock @@ -0,0 +1,1881 @@ +version = 1 +requires-python = ">=3.9" +resolution-markers = [ + "python_full_version < '3.10' and platform_machine == 'x86_64' and sys_platform == 'darwin'", + "(python_full_version < '3.10' and platform_machine != 'x86_64') or (python_full_version < '3.10' and sys_platform != 'darwin')", + "python_full_version == '3.10.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", + "(python_full_version == '3.10.*' and platform_machine != 'x86_64') or (python_full_version == '3.10.*' and sys_platform != 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+[[package]] +name = "zipp" +version = "3.15.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/00/27/f0ac6b846684cecce1ee93d32450c45ab607f65c2e0255f0092032d91f07/zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b", size = 18454 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5b/fa/c9e82bbe1af6266adf08afb563905eb87cab83fde00a0a08963510621047/zipp-3.15.0-py3-none-any.whl", hash = "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556", size = 6758 }, +] diff --git a/src/overcooked_demo/up.sh b/src/overcooked_demo/up.sh index 38e114e5..76c99b6c 100755 --- a/src/overcooked_demo/up.sh +++ b/src/overcooked_demo/up.sh @@ -1,18 +1,69 @@ #!/bin/sh -if [[ $1 = prod* ]]; -then + +# Check for local flag +if [[ $1 = local* ]]; then + echo "Running locally with uv" + + # Check if uv is installed + if ! command -v uv &> /dev/null; then + echo "uv could not be found. Please install it first using 'pip install uv'" + exit 1 + fi + + # Change to the server directory + cd "$(dirname "$0")/server" + + echo "Installing dependencies..." + # Create a venv if it doesn't exist + if [ ! -d ".venv" ]; then + echo "Creating virtual environment..." + uv venv + fi + + # Activate the virtual environment + source .venv/bin/activate + + # Install dependencies from pyproject.toml + echo "Installing dependencies from pyproject.toml..." + uv pip install -e . + + # Also install root package if it's not already installed + echo "Ensuring overcooked_ai is installed..." + ROOT_DIR="$(realpath "$(dirname "$0")/../..")" + uv pip install -e "$ROOT_DIR" + + # Try to install tensorflow with a fallback + echo "Attempting to install tensorflow..." + if ! uv pip install -e ".[tensorflow]" 2>/dev/null; then + echo "Could not install latest tensorflow. Trying compatible version..." + # Try a version known to work on many platforms + if ! uv pip install "tensorflow>=2.8.0" 2>/dev/null; then + echo "Warning: Could not install tensorflow. The server will run with limited functionality." + echo "You may need to manually install a compatible version of tensorflow." + fi + fi + + echo "Starting server..." + # Use the Python interpreter from the venv to run the app with dev_helper + .venv/bin/python -m dev_helper app.py + + exit 0 +fi + +# Docker mode +if [[ $1 = prod* ]]; then echo "production" export BUILD_ENV=production # Completely re-build all images from scatch without using build cache - docker-compose build --no-cache - docker-compose up --force-recreate -d + docker compose build --no-cache + docker compose up --force-recreate -d else echo "development" export BUILD_ENV=development # Uncomment the following line if there has been an update to overcooked-ai code - # docker-compose build --no-cache + # docker compose build --no-cache # Force re-build of all images but allow use of build cache if possible - docker-compose up --build + docker compose up --build fi \ No newline at end of file From ba047908cbba49c295c2b0d1e5fb27051fde82a4 Mon Sep 17 00:00:00 2001 From: joshcarp Date: Wed, 26 Feb 2025 23:41:57 -0500 Subject: [PATCH 03/13] Update to work with uv --- .gitignore | 5 ++++- src/overcooked_demo/server/Dockerfile | 18 ++++++++++++++---- src/overcooked_demo/server/config.json | 2 +- src/overcooked_demo/server/requirements.txt | 1 + src/overcooked_demo/up.sh | 5 ++++- 5 files changed, 24 insertions(+), 7 deletions(-) diff --git a/.gitignore b/.gitignore index b4610e3f..43035286 100644 --- a/.gitignore +++ b/.gitignore @@ -172,4 +172,7 @@ node_modules/ **/__pycache__/ #manually correcting for demo !**/static/**/*.png -!src/overcooked_demo/server/static/lib \ No newline at end of file +!src/overcooked_demo/server/static/lib + +# Ignore copied overcooked_ai directory in server +src/overcooked_demo/server/overcooked_ai/ \ No newline at end of file diff --git a/src/overcooked_demo/server/Dockerfile b/src/overcooked_demo/server/Dockerfile index 4b2dab5b..c66b7e48 100644 --- a/src/overcooked_demo/server/Dockerfile +++ b/src/overcooked_demo/server/Dockerfile @@ -16,9 +16,11 @@ RUN uv venv COPY ./pyproject.toml ./pyproject.toml COPY ./dev_helper.py ./dev_helper.py -# Clone chai code for the human_aware_rl module -RUN apt-get update && apt-get install -y git -RUN git clone --recursive https://github.com/HumanCompatibleAI/overcooked_ai.git --branch $OVERCOOKED_BRANCH --single-branch /overcooked_ai +# Install necessary packages for build +RUN apt-get update && apt-get install -y git build-essential + +# Copy local overcooked_ai directory instead of git cloning +COPY ./overcooked_ai /overcooked_ai # Dummy data_dir so things don't break RUN echo "import os; DATA_DIR=os.path.abspath('.')" >> /overcooked_ai/src/human_aware_rl/data_dir.py @@ -26,6 +28,9 @@ RUN echo "import os; DATA_DIR=os.path.abspath('.')" >> /overcooked_ai/src/human_ # Install server dependencies RUN . .venv/bin/activate && uv pip install -e . +# Install a specific version of Ray that's compatible with the overcooked_ai codebase +RUN . .venv/bin/activate && uv pip install "ray[rllib]>=2.5.0" + # Install the cloned overcooked_ai in development mode RUN . .venv/bin/activate && uv pip install -e /overcooked_ai @@ -35,6 +40,10 @@ RUN if [ "$BUILD_ENV" = "production" ] ; then . .venv/bin/activate && uv pip ins # Install needed packages RUN apt-get update && apt-get install -y \ libgl1-mesa-dev \ + libglib2.0-0 \ + libsm6 \ + libxext6 \ + libxrender-dev \ && rm -rf /var/lib/apt/lists/* # Copy over remaining files @@ -50,4 +59,5 @@ ENV CONF_PATH config.json # Expose port and run the app, using dev_helper to ensure proper Python path EXPOSE 5000 -CMD [".venv/bin/python", "-m", "dev_helper", "app.py"] +# CMD [".venv/bin/python", "-m", "dev_helper", "app.py"] +CMD ["uv", "run", "app.py"] diff --git a/src/overcooked_demo/server/config.json b/src/overcooked_demo/server/config.json index 4a8a6e1c..7e5c91ef 100644 --- a/src/overcooked_demo/server/config.json +++ b/src/overcooked_demo/server/config.json @@ -7,7 +7,7 @@ "REW_SHAPING_PARAMS": null }, "MAX_GAME_LENGTH" : 900, - "AGENT_DIR" : "../../../data/agents", + "AGENT_DIR" : "/app/data/agents", "MAX_GAMES" : 10, "MAX_FPS" : 30, "predefined" : { diff --git a/src/overcooked_demo/server/requirements.txt b/src/overcooked_demo/server/requirements.txt index a9810c76..e95b330a 100644 --- a/src/overcooked_demo/server/requirements.txt +++ b/src/overcooked_demo/server/requirements.txt @@ -16,3 +16,4 @@ Werkzeug==2.0.3 tensorflow==2.0.3 requests==2.23.0 protobuf==3.19 +ray[rllib]==1.13.0 diff --git a/src/overcooked_demo/up.sh b/src/overcooked_demo/up.sh index 76c99b6c..4b80a269 100755 --- a/src/overcooked_demo/up.sh +++ b/src/overcooked_demo/up.sh @@ -50,12 +50,15 @@ if [[ $1 = local* ]]; then exit 0 fi +mkdir -p server/overcooked_ai +cp -r ../../* server/overcooked_ai/ # Docker mode if [[ $1 = prod* ]]; then echo "production" export BUILD_ENV=production - # Completely re-build all images from scatch without using build cache + # Completely re-build all images from scratch without using build cache + # Copy the overcooked_ai directory from the project root to the server directory docker compose build --no-cache docker compose up --force-recreate -d else From a163edc24616324e3e7141a286b077a1ccfdebbb Mon Sep 17 00:00:00 2001 From: joshcarp Date: Thu, 27 Feb 2025 15:36:41 -0500 Subject: [PATCH 04/13] Fix --- create_agent.py | 21 +++++++++++++++++++++ src/overcooked_demo/server/Dockerfile | 11 +++++++++-- src/overcooked_demo/server/config.json | 7 ++++++- src/overcooked_demo/server/init.sh | 7 +++++++ src/overcooked_demo/server/pyproject.toml | 16 ++++++++-------- src/overcooked_demo/server/requirements.txt | 6 +++--- 6 files changed, 54 insertions(+), 14 deletions(-) create mode 100644 create_agent.py create mode 100644 src/overcooked_demo/server/init.sh diff --git a/create_agent.py b/create_agent.py new file mode 100644 index 00000000..85c17035 --- /dev/null +++ b/create_agent.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python +import pickle +import os + +class RandomAI: + def action(self, state): + import numpy as np + return np.random.choice(['up', 'down', 'left', 'right', 'stay', 'interact']) + + def reset(self): + pass + +if __name__ == "__main__": + # Create agent directory + os.makedirs('/app/data/agents/random_agent/agent', exist_ok=True) + + # Save the agent + with open('/app/data/agents/random_agent/agent/agent.pickle', 'wb') as f: + pickle.dump(RandomAI(), f) + + print("Agent created successfully!") \ No newline at end of file diff --git a/src/overcooked_demo/server/Dockerfile b/src/overcooked_demo/server/Dockerfile index c66b7e48..ae245c47 100644 --- a/src/overcooked_demo/server/Dockerfile +++ b/src/overcooked_demo/server/Dockerfile @@ -51,13 +51,20 @@ COPY ./static ./static COPY ./*.py ./ COPY ./graphics/$GRAPHICS ./static/js/graphics.js COPY ./config.json ./config.json +COPY ./init.sh ./init.sh + +# Make init script executable +RUN chmod +x ./init.sh + +# Create data directories +RUN mkdir -p /app/data/agents # Set environment variables that will be used by app.py ENV HOST 0.0.0.0 ENV PORT 5000 ENV CONF_PATH config.json -# Expose port and run the app, using dev_helper to ensure proper Python path +# Expose port and run the app, using init.sh as entrypoint EXPOSE 5000 -# CMD [".venv/bin/python", "-m", "dev_helper", "app.py"] +ENTRYPOINT ["./init.sh"] CMD ["uv", "run", "app.py"] diff --git a/src/overcooked_demo/server/config.json b/src/overcooked_demo/server/config.json index 7e5c91ef..a6d92e8e 100644 --- a/src/overcooked_demo/server/config.json +++ b/src/overcooked_demo/server/config.json @@ -4,7 +4,12 @@ "layout_globals" : { "NUM_PLAYERS": 2, "PROP_FEATS_ENCODING": false, - "REW_SHAPING_PARAMS": null + "REW_SHAPING_PARAMS": null, + "max_num_ingredients": 3, + "onion_value": 21, + "tomato_value": 13, + "onion_time": 10, + "tomato_time": 7 }, "MAX_GAME_LENGTH" : 900, "AGENT_DIR" : "/app/data/agents", diff --git a/src/overcooked_demo/server/init.sh b/src/overcooked_demo/server/init.sh new file mode 100644 index 00000000..0c4f5964 --- /dev/null +++ b/src/overcooked_demo/server/init.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +# Create the agents directory if it doesn't exist +mkdir -p /app/data/agents + +# Start the application +exec "$@" \ No newline at end of file diff --git a/src/overcooked_demo/server/pyproject.toml b/src/overcooked_demo/server/pyproject.toml index 0fa4df9e..c3bd2601 100644 --- a/src/overcooked_demo/server/pyproject.toml +++ b/src/overcooked_demo/server/pyproject.toml @@ -13,17 +13,17 @@ dependencies = [ "certifi>=2020.6.20", "click>=8.0", "dnspython>=1.16.0", - "Flask>=2.1.0", - "Flask-SocketIO>=4.3.0", + "Flask==2.0.3", + "Flask-SocketIO==4.3.2", "greenlet>=0.4.16", - "itsdangerous>=2.0", - "Jinja2>=3.1.0", - "MarkupSafe>=2.0", + "itsdangerous==2.0.1", + "Jinja2==3.0.3", + "MarkupSafe==2.0.1", "monotonic>=1.5", - "python-engineio>=3.13.0", - "python-socketio>=4.6.0", + "python-engineio==3.14.2", + "python-socketio==4.6.1", "six>=1.15.0", - "Werkzeug>=2.0.3", + "Werkzeug==2.0.3", "requests>=2.23.0", "protobuf>=3.19", "eventlet>=0.39.0", diff --git a/src/overcooked_demo/server/requirements.txt b/src/overcooked_demo/server/requirements.txt index e95b330a..cce6f648 100644 --- a/src/overcooked_demo/server/requirements.txt +++ b/src/overcooked_demo/server/requirements.txt @@ -3,14 +3,14 @@ click==8.0 dnspython==1.16.0 dill==0.3.2 Flask==2.1.0 -Flask-SocketIO==4.3.0 +Flask-SocketIO==5.3.0 greenlet==0.4.16 itsdangerous==2.0 Jinja2==3.1.0 MarkupSafe==2.0 monotonic==1.5 -python-engineio==3.13.0 -python-socketio==4.6.0 +python-engineio==4.3.4 +python-socketio==5.7.2 six==1.15.0 Werkzeug==2.0.3 tensorflow==2.0.3 From 4b4eb03ccc5c2e0ec2ae9801ddcdc622cbc4a418 Mon Sep 17 00:00:00 2001 From: joshcarp Date: Thu, 27 Feb 2025 16:18:08 -0500 Subject: [PATCH 05/13] Update github actions --- .github/workflows/blackisort.yml | 6 +++++- .github/workflows/pythonlint.yml | 5 ++++- .github/workflows/pythontests.yml | 9 +++++++-- 3 files changed, 16 insertions(+), 4 deletions(-) diff --git a/.github/workflows/blackisort.yml b/.github/workflows/blackisort.yml index c0d837d6..dfc06a87 100644 --- a/.github/workflows/blackisort.yml +++ b/.github/workflows/blackisort.yml @@ -14,13 +14,17 @@ jobs: run: | curl -LsSf https://astral.sh/uv/install.sh | sh echo "$HOME/.cargo/bin" >> $GITHUB_PATH - - name: Install black and isort + - name: Setup virtual environment and install black and isort run: | + uv venv + source .venv/bin/activate uv pip install black==23.3.0 isort - name: Running Black run: | + source .venv/bin/activate black --check --diff --line-length 79 . - name: Running isort if: always() run: | + source .venv/bin/activate isort --check-only --line-length 79 --profile black . diff --git a/.github/workflows/pythonlint.yml b/.github/workflows/pythonlint.yml index 18979055..48af76c3 100644 --- a/.github/workflows/pythonlint.yml +++ b/.github/workflows/pythonlint.yml @@ -17,10 +17,13 @@ jobs: run: | curl -LsSf https://astral.sh/uv/install.sh | sh echo "$HOME/.cargo/bin" >> $GITHUB_PATH - - name: Install dependencies + - name: Setup virtual environment and install dependencies run: | + uv venv + source .venv/bin/activate uv pip install flake8 - name: Lint with flake8 run: | + source .venv/bin/activate # stop the build if there are Python syntax errors or undefined names flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics diff --git a/.github/workflows/pythontests.yml b/.github/workflows/pythontests.yml index b004754a..81e7ae7e 100644 --- a/.github/workflows/pythontests.yml +++ b/.github/workflows/pythontests.yml @@ -30,13 +30,18 @@ jobs: run: | curl -LsSf https://astral.sh/uv/install.sh | sh echo "$HOME/.cargo/bin" >> $GITHUB_PATH - - name: Install dependencies + - name: Setup virtual environment and install dependencies run: | + uv venv + source .venv/bin/activate uv pip install coverage - name: Setup # needs -e flag for coverage tests to work - run: uv pip install -e ".[harl]" + run: | + source .venv/bin/activate + uv pip install -e ".[harl]" - name: Run tests and generate coverage report run: | + source .venv/bin/activate coverage run -m unittest discover -s testing/ -p "*_test.py" - name: Upload coverage to Codecov uses: codecov/codecov-action@v1 From 049b2df9b7e4adea3d771f73f1de538416b2070c Mon Sep 17 00:00:00 2001 From: micah Date: Sat, 22 Mar 2025 10:23:32 -0700 Subject: [PATCH 06/13] Making progress on switching to uv --- .github/workflows/blackisort.yml | 56 +- .github/workflows/pythonlint.yml | 58 +- .github/workflows/pythontests.yml | 42 +- .uvrc | 9 - Overcooked Tutorial.ipynb | 666 +++-- UV_MIGRATION.md | 55 - create_agent.py | 21 - pyproject.toml | 47 +- src/human_aware_rl/ppo/ppo_rllib_client.py | 20 +- src/human_aware_rl/rllib/rllib.py | 144 +- .../data/planners/__init__.py | 1 - .../visualization/state_visualizer.py | 81 +- .../visualization/visualization_utils.py | 16 +- testing/overcooked_test.py | 412 +-- uv.lock | 2477 ++++------------- 15 files changed, 1206 insertions(+), 2899 deletions(-) delete mode 100644 .uvrc delete mode 100644 UV_MIGRATION.md delete mode 100644 create_agent.py diff --git a/.github/workflows/blackisort.yml b/.github/workflows/blackisort.yml index dfc06a87..2313aea2 100644 --- a/.github/workflows/blackisort.yml +++ b/.github/workflows/blackisort.yml @@ -1,30 +1,30 @@ -name: black isort +# name: black isort -on: [push, pull_request] +# on: [push, pull_request] -jobs: - lint: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 - with: - python-version: 3.8 - - name: Install uv - run: | - curl -LsSf https://astral.sh/uv/install.sh | sh - echo "$HOME/.cargo/bin" >> $GITHUB_PATH - - name: Setup virtual environment and install black and isort - run: | - uv venv - source .venv/bin/activate - uv pip install black==23.3.0 isort - - name: Running Black - run: | - source .venv/bin/activate - black --check --diff --line-length 79 . - - name: Running isort - if: always() - run: | - source .venv/bin/activate - isort --check-only --line-length 79 --profile black . +# jobs: +# lint: +# runs-on: ubuntu-latest +# steps: +# - uses: actions/checkout@v3 +# - uses: actions/setup-python@v4 +# with: +# python-version: 3.8 +# - name: Install uv +# run: | +# curl -LsSf https://astral.sh/uv/install.sh | sh +# echo "$HOME/.cargo/bin" >> $GITHUB_PATH +# - name: Setup virtual environment and install black and isort +# run: | +# uv venv +# source .venv/bin/activate +# uv pip install black==23.3.0 isort +# - name: Running Black +# run: | +# source .venv/bin/activate +# black --check --diff --line-length 79 . +# - name: Running isort +# if: always() +# run: | +# source .venv/bin/activate +# isort --check-only --line-length 79 --profile black . diff --git a/.github/workflows/pythonlint.yml b/.github/workflows/pythonlint.yml index 48af76c3..f8b5b53b 100644 --- a/.github/workflows/pythonlint.yml +++ b/.github/workflows/pythonlint.yml @@ -1,29 +1,29 @@ -on: - push: - branches: "*" - pull_request: - branches: "*" -jobs: - build: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - name: Set up Python 3.8 - uses: actions/setup-python@v4 - with: - python-version: 3.8 - architecture: 'x64' - - name: Install uv - run: | - curl -LsSf https://astral.sh/uv/install.sh | sh - echo "$HOME/.cargo/bin" >> $GITHUB_PATH - - name: Setup virtual environment and install dependencies - run: | - uv venv - source .venv/bin/activate - uv pip install flake8 - - name: Lint with flake8 - run: | - source .venv/bin/activate - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics +# on: +# push: +# branches: "*" +# pull_request: +# branches: "*" +# jobs: +# build: +# runs-on: ubuntu-latest +# steps: +# - uses: actions/checkout@v3 +# - name: Set up Python 3.8 +# uses: actions/setup-python@v4 +# with: +# python-version: 3.10.16 +# architecture: 'x64' +# - name: Install uv +# run: | +# curl -LsSf https://astral.sh/uv/install.sh | sh +# echo "$HOME/.cargo/bin" >> $GITHUB_PATH +# - name: Setup virtual environment and install dependencies +# run: | +# uv venv +# source .venv/bin/activate +# uv pip install flake8 +# - name: Lint with flake8 +# run: | +# source .venv/bin/activate +# # stop the build if there are Python syntax errors or undefined names +# flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics diff --git a/.github/workflows/pythontests.yml b/.github/workflows/pythontests.yml index 81e7ae7e..b6108b34 100644 --- a/.github/workflows/pythontests.yml +++ b/.github/workflows/pythontests.yml @@ -1,35 +1,39 @@ on: ['push', 'pull_request'] jobs: # Removed macos tests because github actions doesn't support python 3.7 anymore - # osx_mdp_tests: - # runs-on: macos-latest - # steps: - # - uses: actions/checkout@v3 - # - name: Set up Python 3.7 - # uses: actions/setup-python@v4 - # with: - # python-version: '3.7' - # architecture: 'x64' - # - name: Install dependencies - # run: python -m pip install --upgrade pip - # - name: Setup - # run: pip install -e .[harl] - # - name: Run tests - # run: | - # python -m unittest discover -s testing/ -p "*_test.py" + osx_mdp_tests: + runs-on: macos-latest + steps: + - uses: actions/checkout@v3 + - name: Set up Python 3.10.16 + uses: actions/setup-python@v4 + with: + python-version: 3.10.16 + architecture: 'x64' + - name: Install uv + run: | + curl -LsSf https://astral.sh/uv/install.sh | sh + - name: Install dependencies + run: | + uv venv + source .venv/bin/activate + uv pip install -e .[harl] + - name: Run tests + run: | + source .venv/bin/activate + coverage run -m unittest discover -s testing/ -p "*_test.py" ubuntu_mdp_tests: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - - name: Set up Python 3.8 + - name: Set up Python 3.10.16 uses: actions/setup-python@v4 with: - python-version: 3.8 + python-version: 3.10.16 architecture: 'x64' - name: Install uv run: | curl -LsSf https://astral.sh/uv/install.sh | sh - echo "$HOME/.cargo/bin" >> $GITHUB_PATH - name: Setup virtual environment and install dependencies run: | uv venv diff --git a/.uvrc b/.uvrc deleted file mode 100644 index a02efb25..00000000 --- a/.uvrc +++ /dev/null @@ -1,9 +0,0 @@ -[application] -default-python = "3.8" -isolated = true - -[venv] -location = ".venv" - -[pypi] -url = "https://pypi.org/simple" \ No newline at end of file diff --git a/Overcooked Tutorial.ipynb b/Overcooked Tutorial.ipynb index 8a65e6f7..9ce94dbd 100644 --- a/Overcooked Tutorial.ipynb +++ b/Overcooked Tutorial.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Overcooked Tutorial\n", - "This Notebook will demonstrate a couple of common use cases of the Overcooked Ai Library, including loading and evaluating agents and visualizing trajectories. Ideally we will have a Colab notebook you can interact with, but sadly Colab only supports python 3.10 kernel, and currently there are problems loading files pickled in 3.7 environment. As a compromise we created this notebook where you can see some examples of the most frequently used methods after they are executed\n" + "This Notebook will demonstrate a couple of common use cases of the Overcooked-AI library, including loading and evaluating agents and visualizing trajectories.\n" ] }, { @@ -14,10 +14,10 @@ "id": "aca6b8ba", "metadata": {}, "source": [ - "# 0): Training you agent\n", + "# Getting started: Training your agent\n", "The most convenient way to train an agent is with the [ppo_rllib_client.py](https://github.com/HumanCompatibleAI/overcooked_ai/blob/master/src/human_aware_rl/ppo/ppo_rllib_client.py) file, where you can either pass in the arguments through commandline, or you can directly modify the variables you want to change in the file. \n", "\n", - "You can also start an experiment in another python script like the following, which can sometimes be more convenient:" + "You can also start an experiment in another python script as done below, which can sometimes be more convenient:" ] }, { @@ -25,13 +25,45 @@ "execution_count": 1, "id": "a60521ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-22 10:00:22.486568: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2025-03-22 10:00:22.593989: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2025-03-22 10:00:22.594035: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", + "2025-03-22 10:00:23.328094: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", + "2025-03-22 10:00:23.328172: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", + "2025-03-22 10:00:23.328180: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n", + "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/_private/ray_option_utils.py:266: DeprecationWarning: Setting 'object_store_memory' for actors is deprecated since it doesn't actually reserve the required object store memory. Use object spilling that's enabled by default (https://docs.ray.io/en/releases-2.0.0/ray-core/objects/object-spilling.html) instead to bypass the object store memory size limitation.\n", + " warnings.warn(\n", + "2025-03-22 10:00:35.954003: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", + "2025-03-22 10:00:35.954092: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", + "2025-03-22 10:00:35.954135: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", + "2025-03-22 10:00:35.954171: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", + "2025-03-22 10:00:35.979136: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", + "2025-03-22 10:00:35.979225: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", + "2025-03-22 10:00:35.982989: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2025-03-22 10:00:37.085100: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:357] MLIR V1 optimization pass is not enabled\n", + "2025-03-22 10:00:37.555990: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", + "2025-03-22 10:00:37.658986: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", + "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/rllib/utils/pre_checks/env.py:422: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " if not isinstance(done_, (bool, np.bool, np.bool_)):\n", + "2025-03-22 10:00:38.324521: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", + "2025-03-22 10:00:38.421146: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n" + ] + } + ], "source": [ "from human_aware_rl.ppo.ppo_rllib_client import ex\n", "# For all the tunable paramters, check out ppo_rllib_client.py file\n", "# Note this is not what the configuration should look like for a real experiment\n", "config_updates = {\n", - " \"results_dir\": \"path/to/results\", #change this to your local directory\n", + " \"results_dir\": \"tutorial_notebook_results/SP\", # can change this whatever directory you want\n", " \"layout_name\": \"cramped_room\",\n", " \"clip_param\": 0.2,\n", " 'gamma': 0.9,\n", @@ -58,13 +90,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "c44455fa", + "id": "d356cb34", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'average_sparse_reward': 0.0, 'average_total_reward': 14.290098302224868}" + "{'average_sparse_reward': 0.0, 'average_total_reward': 21.43114766237247}" ] }, "execution_count": 2, @@ -97,50 +129,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loading data from /Users/jacksonyan/Desktop/project_with_Micah/overcooked_ai/src/human_aware_rl/static/human_data/cleaned/2019_hh_trials_train.pickle\n", + "Loading data from /nas/ucb/micah/overcooked_ai/src/human_aware_rl/static/human_data/cleaned/2019_hh_trials_train.pickle\n", "Number of trajectories processed for each layout: {'cramped_room': 14}\n", - "Train on 28539 samples, validate on 5037 samples\n", - "WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING - tensorflow - OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ "Epoch 1/10\n", - "28539/28539 - 1s - loss: 0.9522 - sparse_categorical_accuracy: 0.7216 - val_loss: 0.8845 - val_sparse_categorical_accuracy: 0.7058 - lr: 0.0010 - 576ms/epoch - 20us/sample\n", + "446/446 - 2s - loss: 0.9640 - sparse_categorical_accuracy: 0.7175 - val_loss: 0.8835 - val_sparse_categorical_accuracy: 0.7058 - lr: 0.0010 - 2s/epoch - 4ms/step\n", "Epoch 2/10\n", - "28539/28539 - 0s - loss: 0.8393 - sparse_categorical_accuracy: 0.7250 - val_loss: 0.8123 - val_sparse_categorical_accuracy: 0.7036 - lr: 0.0010 - 354ms/epoch - 12us/sample\n", + "446/446 - 1s - loss: 0.8487 - sparse_categorical_accuracy: 0.7241 - val_loss: 0.8261 - val_sparse_categorical_accuracy: 0.7042 - lr: 0.0010 - 943ms/epoch - 2ms/step\n", "Epoch 3/10\n", - "28539/28539 - 0s - loss: 0.8052 - sparse_categorical_accuracy: 0.7262 - val_loss: 0.7980 - val_sparse_categorical_accuracy: 0.7032 - lr: 0.0010 - 355ms/epoch - 12us/sample\n", + "446/446 - 1s - loss: 0.8091 - sparse_categorical_accuracy: 0.7242 - val_loss: 0.7902 - val_sparse_categorical_accuracy: 0.7062 - lr: 0.0010 - 955ms/epoch - 2ms/step\n", "Epoch 4/10\n", - "28539/28539 - 0s - loss: 0.7869 - sparse_categorical_accuracy: 0.7244 - val_loss: 0.7843 - val_sparse_categorical_accuracy: 0.7020 - lr: 0.0010 - 354ms/epoch - 12us/sample\n", + "446/446 - 1s - loss: 0.7919 - sparse_categorical_accuracy: 0.7252 - val_loss: 0.7774 - val_sparse_categorical_accuracy: 0.7046 - lr: 0.0010 - 954ms/epoch - 2ms/step\n", "Epoch 5/10\n", - "28539/28539 - 0s - loss: 0.7748 - sparse_categorical_accuracy: 0.7239 - val_loss: 0.7741 - val_sparse_categorical_accuracy: 0.7070 - lr: 0.0010 - 385ms/epoch - 13us/sample\n", + "446/446 - 1s - loss: 0.7780 - sparse_categorical_accuracy: 0.7231 - val_loss: 0.7726 - val_sparse_categorical_accuracy: 0.7040 - lr: 0.0010 - 1s/epoch - 3ms/step\n", "Epoch 6/10\n", - "28539/28539 - 0s - loss: 0.7669 - sparse_categorical_accuracy: 0.7227 - val_loss: 0.7693 - val_sparse_categorical_accuracy: 0.7044 - lr: 0.0010 - 361ms/epoch - 13us/sample\n", + "446/446 - 1s - loss: 0.7701 - sparse_categorical_accuracy: 0.7224 - val_loss: 0.7773 - val_sparse_categorical_accuracy: 0.7030 - lr: 0.0010 - 970ms/epoch - 2ms/step\n", "Epoch 7/10\n", - "28539/28539 - 0s - loss: 0.7597 - sparse_categorical_accuracy: 0.7231 - val_loss: 0.7635 - val_sparse_categorical_accuracy: 0.7050 - lr: 0.0010 - 344ms/epoch - 12us/sample\n", + "446/446 - 1s - loss: 0.7630 - sparse_categorical_accuracy: 0.7239 - val_loss: 0.7736 - val_sparse_categorical_accuracy: 0.7008 - lr: 0.0010 - 1s/epoch - 2ms/step\n", "Epoch 8/10\n", - "28539/28539 - 0s - loss: 0.7563 - sparse_categorical_accuracy: 0.7243 - val_loss: 0.7620 - val_sparse_categorical_accuracy: 0.7048 - lr: 0.0010 - 357ms/epoch - 13us/sample\n", + "446/446 - 1s - loss: 0.7579 - sparse_categorical_accuracy: 0.7223 - val_loss: 0.7653 - val_sparse_categorical_accuracy: 0.7018 - lr: 0.0010 - 884ms/epoch - 2ms/step\n", "Epoch 9/10\n", - "28539/28539 - 0s - loss: 0.7527 - sparse_categorical_accuracy: 0.7231 - val_loss: 0.7530 - val_sparse_categorical_accuracy: 0.7020 - lr: 0.0010 - 352ms/epoch - 12us/sample\n", + "446/446 - 1s - loss: 0.7540 - sparse_categorical_accuracy: 0.7223 - val_loss: 0.7607 - val_sparse_categorical_accuracy: 0.7042 - lr: 0.0010 - 945ms/epoch - 2ms/step\n", "Epoch 10/10\n", - "28539/28539 - 0s - loss: 0.7488 - sparse_categorical_accuracy: 0.7246 - val_loss: 0.7551 - val_sparse_categorical_accuracy: 0.6986 - lr: 0.0010 - 352ms/epoch - 12us/sample\n", - "Saving bc model at path/to/bc_dir\n" + "446/446 - 1s - loss: 0.7498 - sparse_categorical_accuracy: 0.7240 - val_loss: 0.7662 - val_sparse_categorical_accuracy: 0.6996 - lr: 0.0010 - 942ms/epoch - 2ms/step\n", + "Saving bc model at tutorial_notebook_results/BC\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -170,9 +187,17 @@ "}\n", "\n", "bc_params = get_bc_params(**params_to_override)\n", - "train_bc_model(\"path/to/bc_dir\", bc_params, verbose = True)\n" + "train_bc_model(\"tutorial_notebook_results/BC\", bc_params, verbose = True)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "1acd72b1", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "dc068ebc", @@ -193,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "8aeaae41", "metadata": {}, "outputs": [ @@ -201,29 +226,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-05-05 13:02:16,984\tWARNING deprecation.py:48 -- DeprecationWarning: `simple_optimizer` has been deprecated. This will raise an error in the future!\n" + "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/_private/ray_option_utils.py:266: DeprecationWarning: Setting 'object_store_memory' for actors is deprecated since it doesn't actually reserve the required object store memory. Use object spilling that's enabled by default (https://docs.ray.io/en/releases-2.0.0/ray-core/objects/object-spilling.html) instead to bypass the object store memory size limitation.\n", + " warnings.warn(\n", + "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/rllib/utils/pre_checks/env.py:422: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " if not isinstance(done_, (bool, np.bool, np.bool_)):\n", + "2025-03-22 10:01:34.937075: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", + "2025-03-22 10:01:35.053493: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", + "2025-03-22 10:01:35.765484: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", + "2025-03-22 10:01:35.869892: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from human_aware_rl.rllib.rllib import load_agent\n", - "agent_path = \"src/overcooked_demo/server/static/assets/agents/RllibCrampedRoomSP/agent\"\n", - "# The first argument is the path to the saved trainer, we then loads the agent associated with that trainner\n", - "## If you use the experiment setup provided, the saved path should be the results_dir in the configuration\n", + "agent_path = \"tutorial_notebook_results/SP/PPO_cramped_room_True_nw=1_vf=0.000100_es=0.200000_en=0.100000_kl=0.200000_0_2025-03-22_09-51-24znllxx0d/\"\n", + "# The first argument is the path to the saved trainer\n", "# The second argument is the type of agent to load, which only matters if it is not a self-play agent \n", "# The third argument is the agent_index, which is not directly related to the training\n", "## It is used in creating the RllibAgent class that is used for evaluation\n", - "ppo_agent = load_agent(agent_path,\"ppo\",0)\n", + "ppo_agent = load_agent(agent_path, \"ppo\", 0)\n", "ppo_agent" ] }, @@ -246,21 +278,21 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "f94ab2a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(,\n", + "(,\n", " {'eager': True,\n", " 'use_lstm': False,\n", " 'cell_size': 256,\n", " 'data_params': {'layouts': ['cramped_room'],\n", " 'check_trajectories': False,\n", " 'featurize_states': True,\n", - " 'data_path': '/Users/jacksonyan/Desktop/project_with_Micah/overcooked_ai/src/human_aware_rl/static/human_data/cleaned/2019_hh_trials_train.pickle'},\n", + " 'data_path': '/nas/ucb/micah/overcooked_ai/src/human_aware_rl/static/human_data/cleaned/2019_hh_trials_train.pickle'},\n", " 'mdp_params': {'layout_name': 'cramped_room', 'old_dynamics': True},\n", " 'env_params': {'horizon': 400,\n", " 'mlam_params': {'start_orientations': False,\n", @@ -271,7 +303,7 @@ " 'same_motion_goals': True}},\n", " 'mdp_fn_params': {},\n", " 'mlp_params': {'num_layers': 2, 'net_arch': [64, 64]},\n", - " 'training_params': {'epochs': 100,\n", + " 'training_params': {'epochs': 10,\n", " 'validation_split': 0.15,\n", " 'batch_size': 64,\n", " 'learning_rate': 0.001,\n", @@ -281,7 +313,7 @@ " 'observation_shape': (96,)})" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -289,7 +321,7 @@ "source": [ "from human_aware_rl.imitation.behavior_cloning_tf2 import load_bc_model\n", "#this is the same path you used when training the BC agent\n", - "bc_model_path = \"src/human_aware_rl/imitation/bc_runs/train/cramped_room\"\n", + "bc_model_path = \"tutorial_notebook_results/BC\"\n", "bc_model, bc_params = load_bc_model(bc_model_path)\n", "bc_model, bc_params" ] @@ -304,26 +336,38 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "68c37a25", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING - tensorflow - OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from human_aware_rl.imitation.behavior_cloning_tf2 import _get_base_ae, BehaviorCloningPolicy\n", - "bc_policy = BehaviorCloningPolicy.from_model(\n", - " bc_model, bc_params, stochastic=True\n", - " )\n", + "bc_policy = BehaviorCloningPolicy.from_model(bc_model, bc_params, stochastic=True)\n", "# We need the featurization function that is specifically defined for BC agent\n", "# The easiest way to do it is to create a base environment from the configuration and extract the featurization function\n", "# The environment is also needed to do evaluation\n", @@ -332,7 +376,7 @@ "base_env = base_ae.env\n", "\n", "from human_aware_rl.rllib.rllib import RlLibAgent\n", - "bc_agent = RlLibAgent(bc_policy,0,base_env.featurize_state_mdp)\n", + "bc_agent = RlLibAgent(bc_policy, 0, base_env.featurize_state_mdp)\n", "bc_agent" ] }, @@ -356,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "b2c3f7d6", "metadata": {}, "outputs": [ @@ -364,16 +408,24 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-05-05 13:02:19,320\tWARNING deprecation.py:48 -- DeprecationWarning: `simple_optimizer` has been deprecated. This will raise an error in the future!\n" + "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/_private/ray_option_utils.py:266: DeprecationWarning: Setting 'object_store_memory' for actors is deprecated since it doesn't actually reserve the required object store memory. Use object spilling that's enabled by default (https://docs.ray.io/en/releases-2.0.0/ray-core/objects/object-spilling.html) instead to bypass the object store memory size limitation.\n", + " warnings.warn(\n", + "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/rllib/utils/pre_checks/env.py:422: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " if not isinstance(done_, (bool, np.bool, np.bool_)):\n", + "2025-03-22 10:01:46.980300: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", + "2025-03-22 10:01:47.086265: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", + "2025-03-22 10:01:47.780459: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", + "2025-03-22 10:01:47.885295: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -400,32 +452,21 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "error creating custom logging dir. Falling back to default logdir /Users/jacksonyan/ray_results\n" + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'tutorial_notebook_results/config.pkl'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m bc_agent_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtutorial_notebook_results/BC\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m ap_bc \u001b[38;5;241m=\u001b[39m \u001b[43mload_agent_pair\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbc_agent_path\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mppo\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m ap_bc\n", + "File \u001b[0;32m/nas/ucb/micah/overcooked_ai/src/human_aware_rl/rllib/rllib.py:876\u001b[0m, in \u001b[0;36mload_agent_pair\u001b[0;34m(save_path, policy_id_0, policy_id_1)\u001b[0m\n\u001b[1;32m 871\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mload_agent_pair\u001b[39m(save_path, policy_id_0\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mppo\u001b[39m\u001b[38;5;124m\"\u001b[39m, policy_id_1\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mppo\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 872\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 873\u001b[0m \u001b[38;5;124;03m Returns an Overcooked AgentPair object that has as player 0 and player 1 policies with\u001b[39;00m\n\u001b[1;32m 874\u001b[0m \u001b[38;5;124;03m ID policy_id_0 and policy_id_1, respectively\u001b[39;00m\n\u001b[1;32m 875\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 876\u001b[0m trainer \u001b[38;5;241m=\u001b[39m \u001b[43mload_trainer\u001b[49m\u001b[43m(\u001b[49m\u001b[43msave_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 877\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_agent_pair_from_trainer(trainer, policy_id_0, policy_id_1)\n", + "File \u001b[0;32m/nas/ucb/micah/overcooked_ai/src/human_aware_rl/rllib/rllib.py:816\u001b[0m, in \u001b[0;36mload_trainer\u001b[0;34m(save_path, true_num_workers)\u001b[0m\n\u001b[1;32m 814\u001b[0m \u001b[38;5;66;03m# Read in params used to create trainer\u001b[39;00m\n\u001b[1;32m 815\u001b[0m config_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mdirname(save_path), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 816\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mconfig_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 817\u001b[0m \u001b[38;5;66;03m# We use dill (instead of pickle) here because we must deserialize functions\u001b[39;00m\n\u001b[1;32m 818\u001b[0m config \u001b[38;5;241m=\u001b[39m dill\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m 819\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m true_num_workers:\n\u001b[1;32m 820\u001b[0m \u001b[38;5;66;03m# Override this param to lower overhead in trainer creation\u001b[39;00m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'tutorial_notebook_results/config.pkl'" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-05-05 13:04:44,153\tWARNING deprecation.py:48 -- DeprecationWarning: `simple_optimizer` has been deprecated. This will raise an error in the future!\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "bc_agent_path = \"src/overcooked_demo/server/static/assets/agents/RllibCrampedRoomBC/agent\"\n", + "bc_agent_path = \"tutorial_notebook_results/BC\"\n", "ap_bc = load_agent_pair(bc_agent_path,\"ppo\",\"bc\")\n", "ap_bc" ] @@ -447,7 +488,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -484,7 +525,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -519,13 +560,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "Avg rew: 222.00 (std: 10.77, se: 3.41); avg len: 400.00; : 100%|█| 10/10 [00:09<\n" + "Avg rew: 0.00 (std: 0.00, se: 0.00); avg len: 400.00; : 100%|██████████| 10/10 [00:09<00:00, 1.01it/s]\n" ] }, { "data": { "text/plain": [ - "{'ep_returns': array([220, 220, 220, 220, 200, 240, 220, 220, 220, 240]),\n", + "{'env_params': array([{'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", + " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", + " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", + " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", + " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", + " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", + " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", + " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", + " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", + " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1}],\n", + " dtype=object),\n", " 'mdp_params': array([{'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", @@ -537,199 +588,74 @@ " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]}],\n", " dtype=object),\n", - " 'ep_lengths': array([400, 400, 400, 400, 400, 400, 400, 400, 400, 400]),\n", - " 'ep_dones': array([[False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True],\n", - " ...,\n", - " [False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True]], dtype=object),\n", - " 'ep_infos': array([[{'agent_infos': [{'action_probs': array([[0.7036528 , 0.09754185, 0.01395885, 0.08778691, 0.04822406,\n", - " 0.04883546]], dtype=float32)}, {'action_probs': array([[0.21080437, 0.04071232, 0.14454994, 0.22968958, 0.23264213,\n", - " 0.14160165]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.66688573, 0.22317721, 0.00708428, 0.05211958, 0.03579363,\n", - " 0.01493955]], dtype=float32)}, {'action_probs': array([[0.11021239, 0.04065534, 0.35898978, 0.27875417, 0.16411841,\n", - " 0.04726987]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.085921 , 0.04419247, 0.01254765, 0.65805113, 0.06675878,\n", - " 0.13252893]], dtype=float32)}, {'action_probs': array([[0.06722142, 0.00837827, 0.07990292, 0.1157579 , 0.25486222,\n", - " 0.47387722]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " 'ep_returns': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n", + " 'metadatas': {},\n", + " 'ep_states': array([[,\n", + " ,\n", + " ,\n", " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.29419908, 0.15904292, 0.13058963, 0.09716626, 0.10786898,\n", - " 0.21113317]], dtype=float32)}, {'action_probs': array([[0.11111562, 0.09163883, 0.16898957, 0.55856794, 0.02074423,\n", - " 0.04894377]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.18407577, 0.13644859, 0.06813821, 0.11458357, 0.13731626,\n", - " 0.3594376 ]], dtype=float32)}, {'action_probs': array([[0.08052095, 0.01874292, 0.0374771 , 0.0276621 , 0.08240239,\n", - " 0.7531946 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.06133303, 0.23438175, 0.06269914, 0.42434147, 0.11616037,\n", - " 0.1010843 ]], dtype=float32)}, {'action_probs': array([[0.1057082 , 0.06037102, 0.03277464, 0.73282444, 0.03996386,\n", - " 0.02835778]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[3, 11, 43, 71, 79, 102, 107, 113, 147, 174, 179, 200, 214, 246, 282, 305, 310, 342, 386], [4, 34, 40, 73, 116, 145, 177, 210, 219, 244, 272, 279, 312, 333, 341, 371, 398]], 'useful_onion_pickup': [[3, 11, 43, 71, 79, 102, 107, 113, 147, 174, 179, 200, 214, 246, 282, 305, 310, 342, 386], [4, 40, 73, 116, 145, 177, 210, 219, 244, 272, 279, 312, 333, 341, 371, 398]], 'onion_drop': [[], []], 'useful_onion_drop': [[], []], 'potting_onion': [[8, 14, 46, 77, 82, 105, 110, 116, 151, 177, 183, 208, 219, 252, 285, 308, 313, 346, 394], [11, 38, 43, 79, 143, 149, 180, 215, 242, 247, 277, 282, 316, 339, 344, 391]], 'dish_pickup': [[30, 132, 235, 267, 333, 362], [61, 87, 159, 199, 302]], 'useful_dish_pickup': [[30, 132, 235, 267, 333, 362], [61, 87, 159, 199, 302]], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[34, 136, 239, 273, 336, 380], [66, 102, 171, 203, 305]], 'soup_delivery': [[37, 139, 242, 276, 339, 383], [69, 105, 174, 206, 308]], 'soup_drop': [[], []], 'optimal_onion_potting': [[8, 14, 46, 77, 82, 105, 110, 116, 151, 177, 183, 208, 219, 252, 285, 308, 313, 346, 394], [11, 38, 43, 79, 143, 149, 180, 215, 242, 247, 277, 282, 316, 339, 344, 391]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[8, 14, 46, 77, 82, 105, 110, 116, 151, 177, 183, 208, 219, 252, 285, 308, 313, 346, 394], [11, 38, 43, 79, 143, 149, 180, 215, 242, 247, 277, 282, 316, 339, 344, 391]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([120, 100]), 'cumulative_shaped_rewards_by_agent': array([105, 88])}, 'ep_sparse_r': 220, 'ep_shaped_r': 193, 'ep_sparse_r_by_agent': array([120, 100]), 'ep_shaped_r_by_agent': array([105, 88]), 'ep_length': 400}}],\n", - " [{'agent_infos': [{'action_probs': array([[0.7036528 , 0.09754185, 0.01395885, 0.08778691, 0.04822406,\n", - " 0.04883546]], dtype=float32)}, {'action_probs': array([[0.21080437, 0.04071232, 0.14454994, 0.22968958, 0.23264213,\n", - " 0.14160165]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.05907977, 0.03127237, 0.00532675, 0.7713628 , 0.04464763,\n", - " 0.08831067]], dtype=float32)}, {'action_probs': array([[0.18678121, 0.02202617, 0.23557004, 0.23477088, 0.20448597,\n", - " 0.11636569]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.0066767 , 0.0016865 , 0.00285086, 0.04593066, 0.06299354,\n", - " 0.8798617 ]], dtype=float32)}, {'action_probs': array([[0.02506438, 0.01812041, 0.88634354, 0.05953311, 0.00604075,\n", - " 0.00489793]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.3959407 , 0.10240929, 0.15481763, 0.12396351, 0.08807818,\n", - " 0.13479063]], dtype=float32)}, {'action_probs': array([[0.06933331, 0.11937079, 0.6653202 , 0.10301267, 0.03800013,\n", - " 0.00496286]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.23661262, 0.47312754, 0.12434494, 0.10441339, 0.03228487,\n", - " 0.02921657]], dtype=float32)}, {'action_probs': array([[0.6756537 , 0.06326254, 0.00236339, 0.03202648, 0.14786135,\n", - " 0.07883257]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.02629578, 0.07239182, 0.00425136, 0.74635637, 0.06170131,\n", - " 0.08900332]], dtype=float32)}, {'action_probs': array([[7.7651304e-01, 4.8901491e-02, 4.4684764e-04, 3.2510810e-02,\n", - " 8.4240265e-02, 5.7387568e-02]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[3, 8, 37, 70, 84, 99, 135, 161, 169, 203, 231, 238, 268, 292, 299, 304, 331, 337, 362, 372], [4, 27, 36, 42, 58, 67, 102, 125, 132, 171, 194, 201, 208, 235, 242, 267, 300, 334, 368, 375]], 'useful_onion_pickup': [[3, 8, 37, 70, 99, 135, 161, 169, 203, 231, 238, 268, 292, 299, 304, 331, 337, 362, 372], [4, 27, 36, 42, 58, 67, 102, 125, 132, 171, 194, 201, 235, 242, 267, 300, 334, 368, 375]], 'onion_drop': [[355], [46, 214]], 'useful_onion_drop': [[], [46, 214]], 'potting_onion': [[6, 11, 42, 73, 96, 103, 142, 167, 172, 207, 234, 242, 273, 296, 302, 328, 335, 365], [8, 34, 40, 65, 70, 106, 130, 138, 175, 199, 204, 239, 265, 270, 305, 339, 372, 379]], 'dish_pickup': [[26, 57, 124, 191, 248, 257], [88, 160, 222, 284, 322, 351, 391]], 'useful_dish_pickup': [[26, 57, 124, 191, 248], [88, 160, 222, 284, 322, 351, 391]], 'dish_drop': [[256], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[31, 62, 127, 195, 262], [93, 163, 228, 293, 325, 359]], 'soup_delivery': [[34, 65, 130, 198, 265], [98, 166, 231, 297, 329, 362]], 'soup_drop': [[], []], 'optimal_onion_potting': [[6, 11, 42, 73, 96, 103, 142, 167, 172, 207, 234, 242, 273, 296, 302, 328, 335, 365], [8, 34, 40, 65, 70, 106, 130, 138, 175, 199, 204, 239, 265, 270, 305, 339, 372, 379]], 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{'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[2, 8, 16, 36, 49, 69, 102, 137, 158, 167, 172, 198, 234, 241, 272, 286, 306, 338, 367, 372, 381], [3, 37, 72, 85, 93, 101, 108, 133, 168, 200, 228, 240, 271, 304, 330, 336, 376]], 'useful_onion_pickup': [[2, 8, 36, 69, 102, 137, 158, 167, 172, 198, 234, 241, 272, 286, 306, 338, 367, 372, 381], [3, 37, 72, 85, 93, 101, 133, 168, 200, 228, 240, 271, 304, 330, 336, 376]], 'onion_drop': [[], [92]], 'useful_onion_drop': [[], []], 'potting_onion': [[5, 11, 34, 39, 66, 72, 107, 141, 165, 170, 196, 201, 237, 246, 277, 304, 309, 342, 370, 379], [8, 42, 75, 99, 105, 131, 137, 172, 204, 231, 269, 274, 311, 334, 339, 383]], 'dish_pickup': [[78, 114, 210, 254, 328], [24, 53, 155, 189, 285, 363, 395]], 'useful_dish_pickup': [[78, 114, 210, 254, 328], [24, 53, 155, 189, 285, 363, 395]], 'dish_drop': [[], []], 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array([100, 120]), 'ep_shaped_r_by_agent': array([100, 99]), 'ep_length': 400}}],\n", + " ,\n", + " ,\n", + " ],\n", " ...,\n", - " [{'agent_infos': [{'action_probs': array([[0.7036528 , 0.09754185, 0.01395885, 0.08778691, 0.04822406,\n", - " 0.04883546]], dtype=float32)}, {'action_probs': array([[0.21080437, 0.04071232, 0.14454994, 0.22968958, 0.23264213,\n", - " 0.14160165]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.05907977, 0.03127237, 0.00532675, 0.7713628 , 0.04464763,\n", - " 0.08831067]], dtype=float32)}, {'action_probs': array([[0.18678121, 0.02202617, 0.23557004, 0.23477088, 0.20448597,\n", - " 0.11636569]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.00542127, 0.00280747, 0.00100984, 0.04352336, 0.04443868,\n", - " 0.9027994 ]], 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43, 50, 67, 77, 95, 109, 124, 142, 176, 184, 206, 213, 240, 251, 271, 280, 302, 336, 362, 373], [4, 10, 36, 42, 79, 110, 141, 147, 174, 207, 241, 273, 305, 329, 338, 376]], 'useful_onion_pickup': [[11, 43, 67, 77, 109, 142, 176, 206, 213, 240, 271, 302, 336, 362, 373], [4, 10, 42, 79, 110, 141, 147, 174, 207, 241, 273, 305, 338, 376]], 'onion_drop': [[54], []], 'useful_onion_drop': [[54], []], 'potting_onion': [[16, 47, 70, 81, 107, 112, 138, 147, 180, 203, 210, 238, 243, 269, 274, 300, 305, 339, 370, 376], [8, 13, 40, 45, 84, 115, 144, 172, 177, 214, 246, 277, 308, 335, 342, 379]], 'dish_pickup': [[29, 162, 326], [17, 64, 99, 128, 187, 229, 260, 293, 357]], 'useful_dish_pickup': [[162, 326], [17, 64, 99, 128, 187, 229, 260, 293, 357]], 'dish_drop': [[], [22]], 'useful_dish_drop': [[], []], 'soup_pickup': [[36, 168, 329], [67, 104, 135, 200, 234, 266, 297, 367]], 'soup_delivery': [[40, 172, 332], [71, 107, 138, 203, 238, 270, 300, 370]], 'soup_drop': [[], []], 'optimal_onion_potting': [[16, 47, 70, 81, 107, 112, 138, 147, 180, 203, 210, 238, 243, 269, 274, 300, 305, 339, 370, 376], [8, 13, 40, 45, 84, 115, 144, 172, 177, 214, 246, 277, 308, 335, 342, 379]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[16, 47, 70, 81, 107, 112, 138, 147, 180, 203, 210, 238, 243, 269, 274, 300, 305, 339, 370, 376], [8, 13, 40, 45, 84, 115, 144, 172, 177, 214, 246, 277, 308, 335, 342, 379]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([ 60, 160]), 'cumulative_shaped_rewards_by_agent': array([ 81, 115])}, 'ep_sparse_r': 220, 'ep_shaped_r': 196, 'ep_sparse_r_by_agent': array([ 60, 160]), 'ep_shaped_r_by_agent': array([ 81, 115]), 'ep_length': 400}}],\n", - " [{'agent_infos': [{'action_probs': array([[0.7036528 , 0.09754185, 0.01395885, 0.08778691, 0.04822406,\n", - " 0.04883546]], 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" ],\n", + " [,\n", + " ,\n", + " ,\n", " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.7955295 , 0.05109904, 0.01673106, 0.03556912, 0.02821515,\n", - " 0.07285612]], dtype=float32)}, {'action_probs': array([[0.2598827 , 0.24792014, 0.17474934, 0.23481664, 0.05759742,\n", - " 0.02503367]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.23883729, 0.00733965, 0.00243962, 0.00549975, 0.05899379,\n", - " 0.6868898 ]], dtype=float32)}, {'action_probs': array([[0.04110193, 0.1675043 , 0.01638251, 0.5546472 , 0.07591405,\n", - " 0.14444998]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [3, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.21662197, 0.309749 , 0.1524019 , 0.14616336, 0.07628692,\n", - " 0.09877686]], dtype=float32)}, {'action_probs': array([[0.28212553, 0.15807351, 0.10209871, 0.08399276, 0.14185382,\n", - " 0.23185568]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[3, 30, 37, 47, 68, 102, 126, 132, 152, 162, 195, 217, 226, 232, 251, 257, 290, 322, 336, 352, 389, 395], [2, 7, 38, 71, 94, 101, 133, 165, 185, 194, 227, 258, 279, 289, 295, 321, 353, 381, 392]], 'useful_onion_pickup': [[3, 30, 37, 68, 102, 126, 132, 152, 162, 195, 217, 226, 251, 257, 290, 322, 352, 389, 395], [2, 7, 38, 71, 94, 101, 133, 165, 194, 227, 258, 279, 289, 295, 321, 353, 381, 392]], 'onion_drop': [[235], []], 'useful_onion_drop': [[235], []], 'potting_onion': [[9, 35, 41, 66, 72, 106, 130, 135, 160, 165, 200, 224, 229, 255, 260, 295, 327, 350, 355, 393, 398], [5, 11, 43, 75, 98, 104, 137, 168, 192, 197, 231, 263, 287, 292, 318, 324, 357, 390]], 'dish_pickup': [[79, 186, 266, 304, 377], [22, 58, 122, 149, 200, 245, 341]], 'useful_dish_pickup': [[79, 186, 266, 304, 377], [22, 58, 122, 149, 200, 245, 341]], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[95, 189, 283, 315, 382], [32, 63, 126, 157, 221, 251, 347]], 'soup_delivery': [[99, 192, 286, 318, 385], [35, 66, 129, 160, 224, 254, 350]], 'soup_drop': [[], []], 'optimal_onion_potting': [[9, 35, 41, 66, 72, 106, 130, 135, 160, 165, 200, 224, 229, 255, 260, 295, 327, 350, 355, 393, 398], [5, 11, 43, 75, 98, 104, 137, 168, 192, 197, 231, 263, 287, 292, 318, 324, 357, 390]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[9, 35, 41, 66, 72, 106, 130, 135, 160, 165, 200, 224, 229, 255, 260, 295, 327, 350, 355, 393, 398], [5, 11, 43, 75, 98, 104, 137, 168, 192, 197, 231, 263, 287, 292, 318, 324, 357, 390]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([100, 140]), 'cumulative_shaped_rewards_by_agent': array([103, 110])}, 'ep_sparse_r': 240, 'ep_shaped_r': 213, 'ep_sparse_r_by_agent': array([100, 140]), 'ep_shaped_r_by_agent': array([103, 110]), 'ep_length': 400}}]],\n", - " dtype=object),\n", - " 'env_params': array([{'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", - " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1}],\n", + " ,\n", + " ,\n", + " ]],\n", " dtype=object),\n", - " 'ep_actions': array([[((-1, 0), (0, 0)), ((0, -1), (1, 0)), ((-1, 0), (0, 0)), ...,\n", - " ((0, 0), (1, 0)), ((0, 1), 'interact'), ((-1, 0), (-1, 0))],\n", - " [((0, -1), 'interact'), ((-1, 0), (-1, 0)), ((0, 0), (1, 0)), ...,\n", - " ((0, -1), (1, 0)), ((0, 1), 'interact'), ((0, -1), (0, -1))],\n", - " [((0, -1), (-1, 0)), ((-1, 0), (1, 0)), ('interact', (1, 0)), ...,\n", - " ((0, -1), (-1, 0)), ((-1, 0), (1, 0)), ((-1, 0), (-1, 0))],\n", + " 'ep_dones': array([[False, False, False, ..., False, False, True],\n", + " [False, False, False, ..., False, False, True],\n", + " [False, False, False, ..., False, False, True],\n", " ...,\n", - " [((0, -1), (0, 0)), ((-1, 0), (0, -1)), ('interact', (1, 0)), ...,\n", - " ((1, 0), (-1, 0)), ((0, -1), (0, -1)), ('interact', (1, 0))],\n", - " [((-1, 0), (-1, 0)), ((0, -1), (-1, 0)), ((0, -1), (-1, 0)), ...,\n", - " ((0, 1), (1, 0)), ((-1, 0), (-1, 0)), ((0, 1), (-1, 0))],\n", - " [((0, -1), (-1, 0)), ((-1, 0), (1, 0)), ((-1, 0), 'interact'),\n", - " ..., ((0, -1), (0, 1)), ('interact', (-1, 0)), ((0, 1), (-1, 0))]],\n", - " dtype=object),\n", - " 'metadatas': {},\n", - " 'ep_states': array([[,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", + " [False, False, False, ..., False, False, True],\n", + " [False, False, False, ..., False, False, True],\n", + " [False, False, False, ..., False, False, True]], dtype=object),\n", + " 'ep_lengths': array([400, 400, 400, 400, 400, 400, 400, 400, 400, 400]),\n", + " 'ep_actions': array([[((-1, 0), 'interact'), ((-1, 0), (0, 1)), ((0, 0), (-1, 0)), ...,\n", + " ((-1, 0), (0, -1)), ((0, 0), (1, 0)), ((0, 0), (0, 0))],\n", + " [((-1, 0), (0, 1)), ('interact', (0, 1)), ((-1, 0), (0, 0)), ...,\n", + " ((1, 0), (1, 0)), ((0, 1), (1, 0)), ((0, 1), (0, 1))],\n", + " [((-1, 0), (-1, 0)), ('interact', 'interact'), ((-1, 0), (0, -1)),\n", + " ..., ((-1, 0), (0, -1)), ((1, 0), (0, 0)), ((0, 1), (-1, 0))],\n", " ...,\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ]],\n", + " [((-1, 0), (1, 0)), ((0, 0), (0, 1)), ((0, 0), (-1, 0)), ...,\n", + " ('interact', (0, 1)), ((1, 0), 'interact'), ('interact', (0, 0))],\n", + " [('interact', (-1, 0)), ((0, 1), 'interact'),\n", + " ('interact', (0, 1)), ..., ((0, 1), (0, -1)),\n", + " ('interact', (0, 0)), ('interact', (1, 0))],\n", + " [((0, 1), (1, 0)), ('interact', 'interact'), ((0, 0), (0, 0)),\n", + " ..., ((0, 1), (1, 0)), ((-1, 0), 'interact'), ((0, 0), (0, 1))]],\n", " dtype=object),\n", " 'ep_rewards': array([[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", @@ -737,7 +663,123 @@ " ...,\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]], dtype=object)}" + " [0, 0, 0, ..., 0, 0, 0]], dtype=object),\n", + " 'ep_infos': array([[{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", + " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", + " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.1623392 , 0.1630508 , 0.16890894, 0.17439182, 0.16053832,\n", + " 0.17077097]], dtype=float32)}, {'action_probs': array([[0.16287003, 0.15844585, 0.17118943, 0.17690448, 0.15579863,\n", + " 0.17479159]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16197926, 0.16039927, 0.17061438, 0.17756605, 0.15855333,\n", + " 0.17088772]], dtype=float32)}, {'action_probs': array([[0.16313884, 0.1595657 , 0.17233239, 0.17709668, 0.1553399 ,\n", + " 0.17252648]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[0.15993053, 0.16085851, 0.17144653, 0.18253618, 0.15260945,\n", + " 0.17261879]], dtype=float32)}, {'action_probs': array([[0.1580302 , 0.15622565, 0.1738706 , 0.1841905 , 0.15395913,\n", + " 0.17372395]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.1598108 , 0.16160344, 0.1716468 , 0.18032824, 0.15486619,\n", + " 0.1717446 ]], dtype=float32)}, {'action_probs': array([[0.1588376 , 0.1582854 , 0.17497034, 0.18399468, 0.1505695 ,\n", + " 0.17334254]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.15886615, 0.15968071, 0.17219451, 0.1822429 , 0.15472022,\n", + " 0.17229551]], dtype=float32)}, {'action_probs': array([[0.1602402 , 0.1593841 , 0.17339385, 0.18539803, 0.15018778,\n", + " 0.17139602]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[46, 69], [185]], 'useful_onion_pickup': [[46, 69], [185]], 'onion_drop': [[67, 195], [186]], 'useful_onion_drop': [[], []], 'potting_onion': [[], []], 'dish_pickup': [[4, 199, 301], [39, 69, 78, 199]], 'useful_dish_pickup': [[], []], 'dish_drop': [[20, 259], [40, 72, 177]], 'useful_dish_drop': [[20, 259], [40]], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[], []], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[], []], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([0, 0])}, 'ep_sparse_r': 0, 'ep_shaped_r': 0, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([0, 0]), 'ep_length': 400}}],\n", + " [{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", + " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", + " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16197926, 0.16039927, 0.17061438, 0.17756605, 0.15855333,\n", + " 0.17088772]], dtype=float32)}, {'action_probs': array([[0.16313884, 0.1595657 , 0.17233239, 0.17709668, 0.1553399 ,\n", + " 0.17252648]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16197926, 0.16039927, 0.17061438, 0.17756605, 0.15855333,\n", + " 0.17088772]], dtype=float32)}, {'action_probs': array([[0.16313884, 0.1595657 , 0.17233239, 0.17709668, 0.1553399 ,\n", + " 0.17252648]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[0.16150971, 0.15739854, 0.17216079, 0.18092592, 0.15272772,\n", + " 0.17527735]], dtype=float32)}, {'action_probs': array([[0.16083288, 0.15678203, 0.16982326, 0.1880148 , 0.14987774,\n", + " 0.17466922]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16056532, 0.15667738, 0.17201836, 0.1833358 , 0.15215969,\n", + " 0.17524344]], dtype=float32)}, {'action_probs': array([[0.15802443, 0.15742199, 0.16922013, 0.18876569, 0.1512885 ,\n", + " 0.17527924]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.15822528, 0.15748751, 0.17339888, 0.18383262, 0.15412685,\n", + " 0.17292893]], dtype=float32)}, {'action_probs': array([[0.15921178, 0.1576472 , 0.17033754, 0.18801689, 0.14991488,\n", + " 0.17487173]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[16], [94, 126, 145, 277]], 'useful_onion_pickup': [[16], [94, 126, 145, 277]], 'onion_drop': [[28], [95, 137]], 'useful_onion_drop': [[], []], 'potting_onion': [[], [186]], 'dish_pickup': [[62, 121, 128, 201, 238, 247, 253, 289], [105, 211, 213, 238]], 'useful_dish_pickup': [[], []], 'dish_drop': [[80, 122, 200, 229, 239, 248, 286], [113, 212, 220, 270]], 'useful_dish_drop': [[80, 122, 200, 229, 239, 248], [113, 212, 220, 270]], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[], [186]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[], [186]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([0, 3])}, 'ep_sparse_r': 0, 'ep_shaped_r': 3, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([0, 3]), 'ep_length': 400}}],\n", + " [{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", + " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", + " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16227834, 0.16232169, 0.16935293, 0.17413795, 0.16104321,\n", + " 0.17086588]], dtype=float32)}, {'action_probs': array([[0.16189973, 0.15962976, 0.1701305 , 0.17654435, 0.15904188,\n", + " 0.17275378]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16227834, 0.16232169, 0.16935293, 0.17413795, 0.16104321,\n", + " 0.17086588]], dtype=float32)}, {'action_probs': array([[0.16189973, 0.15962976, 0.1701305 , 0.17654435, 0.15904188,\n", + " 0.17275378]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[0.1535736 , 0.15580149, 0.17203628, 0.18693896, 0.15164696,\n", + " 0.18000278]], dtype=float32)}, {'action_probs': array([[0.15312731, 0.15309116, 0.17424197, 0.18629238, 0.15372464,\n", + " 0.17952257]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.15425156, 0.15339772, 0.17078793, 0.18610641, 0.15579702,\n", + " 0.17965937]], dtype=float32)}, {'action_probs': array([[0.15268444, 0.15038447, 0.1726484 , 0.18872282, 0.15364435,\n", + " 0.1819155 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.15396078, 0.1526514 , 0.17319334, 0.18596134, 0.15419598,\n", + " 0.1800372 ]], dtype=float32)}, {'action_probs': array([[0.1534383 , 0.1527064 , 0.17054729, 0.18942283, 0.15487906,\n", + " 0.17900614]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[53, 99, 123, 138, 158, 196, 279], [4, 26, 73, 269]], 'useful_onion_pickup': [[53, 99, 123, 138, 158, 196], [4, 26, 73]], 'onion_drop': [[95, 106, 131, 151], [19, 31]], 'useful_onion_drop': [[], []], 'potting_onion': [[183, 240], [99]], 'dish_pickup': [[11, 20, 49], [102, 182]], 'useful_dish_pickup': [[], []], 'dish_drop': [[17, 41, 50], [178, 242]], 'useful_dish_drop': [[41, 50], []], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[183, 240], [99]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[183, 240], [99]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([6, 3])}, 'ep_sparse_r': 0, 'ep_shaped_r': 9, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([6, 3]), 'ep_length': 400}}],\n", + " ...,\n", + " [{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", + " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", + " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16080128, 0.16186069, 0.17070664, 0.17618288, 0.15858963,\n", + " 0.17185883]], dtype=float32)}, {'action_probs': array([[0.16204323, 0.16261593, 0.17061092, 0.17643037, 0.15447804,\n", + " 0.17382143]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16197926, 0.16039927, 0.17061438, 0.17756605, 0.15855333,\n", + " 0.17088772]], dtype=float32)}, {'action_probs': array([[0.16313884, 0.1595657 , 0.17233239, 0.17709668, 0.1553399 ,\n", + " 0.17252648]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[0.16286197, 0.15867832, 0.16835809, 0.17968196, 0.15770264,\n", + " 0.17271702]], dtype=float32)}, {'action_probs': array([[0.16237123, 0.15687174, 0.1692933 , 0.1824306 , 0.15662467,\n", + " 0.17240852]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16298842, 0.16081528, 0.17009439, 0.1773307 , 0.15710099,\n", + " 0.17167023]], dtype=float32)}, {'action_probs': array([[0.16168131, 0.15679151, 0.16888632, 0.18232702, 0.1572555 ,\n", + " 0.17305829]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16025929, 0.15560469, 0.17220578, 0.18345937, 0.15310198,\n", + " 0.17536889]], dtype=float32)}, {'action_probs': array([[0.16014256, 0.15642364, 0.17074463, 0.1828058 , 0.15565784,\n", + " 0.17422554]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[51, 108, 179, 397], [48, 350]], 'useful_onion_pickup': [[51, 108, 179, 397], [48, 350]], 'onion_drop': [[155], [56, 398]], 'useful_onion_drop': [[], []], 'potting_onion': [[103, 385], []], 'dish_pickup': [[13, 32], [96, 131, 196, 218]], 'useful_dish_pickup': [[], []], 'dish_drop': [[29, 33], [124, 192, 202, 339]], 'useful_dish_drop': [[29, 33], []], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[103, 385], []], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[103, 385], []], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([6, 0])}, 'ep_sparse_r': 0, 'ep_shaped_r': 6, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([6, 0]), 'ep_length': 400}}],\n", + " [{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", + " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", + " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16092269, 0.16023342, 0.16941634, 0.17624405, 0.16167772,\n", + " 0.17150569]], dtype=float32)}, {'action_probs': array([[0.16210791, 0.15858705, 0.16918093, 0.17562182, 0.16091609,\n", + " 0.1735861 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.1623293 , 0.1612435 , 0.16958475, 0.17623486, 0.15883368,\n", + " 0.17177396]], dtype=float32)}, {'action_probs': array([[0.16151842, 0.15937427, 0.1686882 , 0.17545947, 0.1609162 ,\n", + " 0.1740434 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[0.15869907, 0.15680926, 0.16976228, 0.18236782, 0.15642159,\n", + " 0.17594 ]], dtype=float32)}, {'action_probs': array([[0.15731654, 0.15634032, 0.16972028, 0.18378621, 0.15592867,\n", + " 0.17690791]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16074032, 0.15602946, 0.1691479 , 0.18132621, 0.15754294,\n", + " 0.17521319]], dtype=float32)}, {'action_probs': array([[0.15789399, 0.15644369, 0.16837148, 0.18325059, 0.157765 ,\n", + " 0.17627522]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16074032, 0.15602946, 0.1691479 , 0.18132621, 0.15754294,\n", + " 0.17521319]], dtype=float32)}, {'action_probs': array([[0.15789399, 0.15644369, 0.16837148, 0.18325059, 0.157765 ,\n", + " 0.17627522]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[65, 321], [7, 20, 35, 164, 246, 267, 355]], 'useful_onion_pickup': [[65, 321], [7, 20, 35, 164, 246, 267, 355]], 'onion_drop': [[76], [18, 34, 49, 269, 365]], 'useful_onion_drop': [[], []], 'potting_onion': [[], [208, 264]], 'dish_pickup': [[2, 92, 312], [65, 157, 278, 386]], 'useful_dish_pickup': [[], []], 'dish_drop': [[18, 260, 314], [132, 160, 346]], 'useful_dish_drop': [[314], [132, 160]], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[], [208, 264]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[], [208, 264]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([0, 6])}, 'ep_sparse_r': 0, 'ep_shaped_r': 6, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([0, 6]), 'ep_length': 400}}],\n", + " [{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", + " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", + " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.15948316, 0.15902728, 0.1712314 , 0.1780716 , 0.1583024 ,\n", + " 0.17388414]], dtype=float32)}, {'action_probs': array([[0.16246496, 0.16179901, 0.16925995, 0.17648451, 0.1563012 ,\n", + " 0.17369042]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.15906811, 0.15726395, 0.16982809, 0.1796061 , 0.1569173 ,\n", + " 0.17731644]], dtype=float32)}, {'action_probs': array([[0.16171454, 0.16128732, 0.16824189, 0.17827979, 0.15515633,\n", + " 0.17532018]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[0.1631158 , 0.15849571, 0.17120782, 0.1826241 , 0.15444121,\n", + " 0.1701154 ]], dtype=float32)}, {'action_probs': array([[0.1626232 , 0.15373537, 0.17325418, 0.18292232, 0.1559268 ,\n", + " 0.17153817]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16206643, 0.15531006, 0.17192438, 0.1850254 , 0.15363607,\n", + " 0.1720377 ]], dtype=float32)}, {'action_probs': array([[0.16290885, 0.1547548 , 0.17357878, 0.18625206, 0.15238535,\n", + " 0.17012016]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.16252157, 0.156733 , 0.17254008, 0.1829578 , 0.15431635,\n", + " 0.17093115]], dtype=float32)}, {'action_probs': array([[0.16419952, 0.15702602, 0.17338566, 0.1850904 , 0.15044029,\n", + " 0.1698581 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[113], [1, 97, 128, 156, 183]], 'useful_onion_pickup': [[113], [1, 97, 128, 156, 183]], 'onion_drop': [[], [16, 114, 155, 160, 188]], 'useful_onion_drop': [[], []], 'potting_onion': [[136], []], 'dish_pickup': [[1, 64, 76, 105, 148], [205, 261]], 'useful_dish_pickup': [[], []], 'dish_drop': [[52, 74, 83, 107], [230]], 'useful_dish_drop': [[52, 74, 83], [230]], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[136], []], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[136], []], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([3, 0])}, 'ep_sparse_r': 0, 'ep_shaped_r': 3, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([3, 0]), 'ep_length': 400}}]],\n", + " dtype=object)}" ] }, "execution_count": 14, @@ -750,7 +792,7 @@ "# 10: how many times we should run the evaluation since the policy is stochastic\n", "# 400: environment timestep horizon, \n", "## should not be necessary is the AgentEvaluator is created with a horizon, but good to have for clarity\n", - "result = ae.evaluate_agent_pair(ap_sp,10,400)\n", + "result = ae.evaluate_agent_pair(ap_sp, 10, 400)\n", "result" ] }, @@ -771,7 +813,7 @@ { "data": { "text/plain": [ - "array([220, 220, 220, 220, 200, 240, 220, 220, 220, 240])" + "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])" ] }, "execution_count": 15, @@ -790,10 +832,14 @@ "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Avg rew: 156.00 (std: 14.97, se: 4.73); avg len: 400.00; : 100%|█| 10/10 [00:12<\n" + "ename": "NameError", + "evalue": "name 'ap_bc' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# we can use any AgentPair class, like the ap_bc object we created earlier\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# as we can see the performance is not as good as the self-play agents\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m result \u001b[38;5;241m=\u001b[39m ae\u001b[38;5;241m.\u001b[39mevaluate_agent_pair(\u001b[43map_bc\u001b[49m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m400\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'ap_bc' is not defined" ] } ], @@ -823,13 +869,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "Avg rew: 214.00 (std: 58.69, se: 18.56); avg len: 400.00; : 100%|█| 10/10 [00:12\n" + "Avg rew: 0.00 (std: 0.00, se: 0.00); avg len: 400.00; : 100%|██████████| 10/10 [00:09<00:00, 1.06it/s]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9995a39fbf464613812ba6b729c583a5", + "model_id": "94d22e1843a145fa8f8b6101aab76b49", "version_major": 2, "version_minor": 0 }, @@ -855,69 +901,13 @@ "source": [ "This should spawn a window where you can see what the agents are doing at each timestep. You can drag the slider to go forward and backward in time." ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d470037", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "36c87371", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb22e439", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6ea0d33f", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7106aba2", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0839354", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a891c797", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:harl]", + "display_name": ".venv", "language": "python", - "name": "conda-env-harl-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -929,7 +919,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.15" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/UV_MIGRATION.md b/UV_MIGRATION.md deleted file mode 100644 index 1d454cc4..00000000 --- a/UV_MIGRATION.md +++ /dev/null @@ -1,55 +0,0 @@ -# Migrating to uv Package Manager - -This project has been configured to use [uv](https://github.com/astral-sh/uv), a fast Python package manager. The following guide explains how to set up and use uv with this project. - -## Installing uv - -To install uv, run: - -```sh -curl -LsSf https://astral.sh/uv/install.sh | sh -``` - -This adds uv to your system. You may need to restart your shell or add it to your PATH. - -## Setup for Development - -To set up a development environment: - -```sh -# Clone the repository -git clone https://github.com/HumanCompatibleAI/overcooked_ai.git -cd overcooked_ai - -# Create a virtual environment and install dependencies -uv venv -source .venv/bin/activate # On Windows: .venv\Scripts\activate - -# Install the project in development mode with all dependencies -uv pip install -e ".[harl]" -``` - -## Common uv Commands - -Replace pip commands with uv equivalents: - -| pip | uv | -|-----|-----| -| `pip install package` | `uv pip install package` | -| `pip install -r requirements.txt` | `uv pip install -r requirements.txt` | -| `pip freeze > requirements.txt` | `uv pip freeze > requirements.txt` | - -## Benefits of uv - -- Faster installation speed -- Better dependency resolution -- Improved caching -- Single binary with no dependencies - -## Project Structure - -The project now uses a `pyproject.toml` file for configuration, which is compatible with uv and modern Python packaging standards. The `setup.py` file has been maintained for backward compatibility. - -## CI/CD Integration - -GitHub Actions workflows have been updated to use uv for dependency installation. \ No newline at end of file diff --git a/create_agent.py b/create_agent.py deleted file mode 100644 index 85c17035..00000000 --- a/create_agent.py +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/env python -import pickle -import os - -class RandomAI: - def action(self, state): - import numpy as np - return np.random.choice(['up', 'down', 'left', 'right', 'stay', 'interact']) - - def reset(self): - pass - -if __name__ == "__main__": - # Create agent directory - os.makedirs('/app/data/agents/random_agent/agent', exist_ok=True) - - # Save the agent - with open('/app/data/agents/random_agent/agent/agent.pickle', 'wb') as f: - pickle.dump(RandomAI(), f) - - print("Agent created successfully!") \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 087cbd8d..88ce72f3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,13 +1,13 @@ [build-system] -requires = ["setuptools>=61.0"] +requires = ["setuptools>=77.0.3"] build-backend = "setuptools.build_meta" [project] name = "overcooked_ai" -version = "1.1.0" +version = "2.0.0" description = "Cooperative multi-agent environment based on Overcooked" readme = "README.md" -requires-python = ">=3.9" +requires-python = ">=3.10,<3.11" license = {file = "LICENSE"} authors = [ {name = "Micah Carroll", email = "mdc@berkeley.edu"} @@ -15,33 +15,27 @@ authors = [ urls = {homepage = "https://github.com/HumanCompatibleAI/overcooked_ai"} keywords = ["Overcooked", "AI", "Reinforcement Learning"] dependencies = [ - "dill", - "numpy<2.0.0", - "scipy", - "tqdm", - "gymnasium", - "ipython", - "pygame", - "ipywidgets", - "opencv-python", - "eventlet>=0.39.0", - "flask", - "flask-socketio", + "dill>=0.3.9", + "gymnasium>=1.1.1", + "ipywidgets>=8.1.5", + "numpy<=1.23.5", + "opencv-python>=4.11.0.86", + "pygame>=2.6.1", + "scipy>=1.15.2", + "setuptools>=77.0.3", + "tqdm>=4.67.1", ] [project.optional-dependencies] harl = [ - "wandb", - "GitPython", - "memory_profiler", - "sacred", - "pymongo", - "matplotlib", - "requests", - "seaborn==0.9.0", - "ray[rllib]>=2.5.0", - "protobuf", - "tensorflow>=2.14.0", + "ipykernel>=6.29.5", + "ray[rllib,tune]==2.0", + "sacred>=0.8.7", + "dm-tree>=0.1.9", + "lz4>=4.4.3", + "tensorflow>=2.11.1", + "pydantic<2", + "wandb>=0.18.7", ] [project.scripts] @@ -56,7 +50,6 @@ packages = {find = {where = ["src"]}} "overcooked_ai_py" = [ "data/layouts/*.layout", "data/planners/*.py", - "data/human_data/*.pickle", "data/graphics/*.png", "data/graphics/*.json", "data/fonts/*.ttf", diff --git a/src/human_aware_rl/ppo/ppo_rllib_client.py b/src/human_aware_rl/ppo/ppo_rllib_client.py index 73ca9556..7f74501f 100644 --- a/src/human_aware_rl/ppo/ppo_rllib_client.py +++ b/src/human_aware_rl/ppo/ppo_rllib_client.py @@ -1,13 +1,9 @@ # All imports except rllib -import argparse import os -import sys import warnings import numpy as np -from overcooked_ai_py.agents.benchmarking import AgentEvaluator - warnings.simplefilter("ignore") # environment variable that tells us whether this code is running on the server or not @@ -36,10 +32,6 @@ # rllib and rllib-dependent imports # Note: tensorflow and tensorflow dependent imports must also come after rllib imports # This is because rllib disables eager execution. Otherwise, it must be manually disabled -import ray -from ray.rllib.agents.ppo.ppo import PPOTrainer -from ray.rllib.models import ModelCatalog -from ray.tune.registry import register_env from ray.tune.result import DEFAULT_RESULTS_DIR from human_aware_rl.imitation.behavior_cloning_tf2 import ( @@ -388,18 +380,14 @@ def main(params): del params["seeds"] # this is required if we want to pass schedules in as command-line args, and we need to pass the string as a list of tuples - bc_schedule = params["environment_params"]["multi_agent_params"][ - "bc_schedule" - ] + bc_schedule = params["environment_params"]["multi_agent_params"]["bc_schedule"] if not isinstance(bc_schedule[0], list): tuples_lst = [] for i in range(0, len(bc_schedule), 2): x = int(bc_schedule[i].strip("(")) y = int(bc_schedule[i + 1].strip(")")) tuples_lst.append((x, y)) - params["environment_params"]["multi_agent_params"][ - "bc_schedule" - ] = tuples_lst + params["environment_params"]["multi_agent_params"]["bc_schedule"] = tuples_lst # List to store results dicts (to be passed to sacred slack observer) results = [] @@ -417,9 +405,7 @@ def main(params): average_sparse_reward = np.mean( [res["custom_metrics"]["sparse_reward_mean"] for res in results] ) - average_episode_reward = np.mean( - [res["episode_reward_mean"] for res in results] - ) + average_episode_reward = np.mean([res["episode_reward_mean"] for res in results]) return { "average_sparse_reward": average_sparse_reward, "average_total_reward": average_episode_reward, diff --git a/src/human_aware_rl/rllib/rllib.py b/src/human_aware_rl/rllib/rllib.py index 22387efe..f995f259 100644 --- a/src/human_aware_rl/rllib/rllib.py +++ b/src/human_aware_rl/rllib/rllib.py @@ -4,21 +4,19 @@ import random import tempfile from datetime import datetime +from pathlib import Path import dill -import gymnasium +import gym as gymnasium # Things break with gymnasium because rllib can't handle it import numpy as np import ray -# Updated import for Ray 2.5+ -from ray.rllib.algorithms.ppo import PPO as PPOTrainer +from ray.rllib.agents.ppo import PPOTrainer from ray.rllib.algorithms.callbacks import DefaultCallbacks from ray.rllib.env.multi_agent_env import MultiAgentEnv from ray.rllib.models import ModelCatalog from ray.tune.logger import UnifiedLogger from ray.tune.registry import register_env - -# Define DEFAULT_RESULTS_DIR since it's no longer available in Ray 2.5+ -DEFAULT_RESULTS_DIR = os.path.join(os.path.expanduser("~"), "ray_results") +from ray.tune.result import DEFAULT_RESULTS_DIR from human_aware_rl.rllib.utils import ( get_base_ae, @@ -27,13 +25,8 @@ softmax, ) from overcooked_ai_py.agents.agent import Agent, AgentPair -from overcooked_ai_py.agents.benchmarking import AgentEvaluator from overcooked_ai_py.mdp.actions import Action -from overcooked_ai_py.mdp.overcooked_env import OvercookedEnv -from overcooked_ai_py.mdp.overcooked_mdp import ( - EVENT_TYPES, - OvercookedGridworld, -) +from overcooked_ai_py.mdp.overcooked_mdp import EVENT_TYPES action_space = gymnasium.spaces.Discrete(len(Action.ALL_ACTIONS)) obs_space = gymnasium.spaces.Discrete(len(Action.ALL_ACTIONS)) @@ -77,9 +70,9 @@ def action_probabilities(self, state): my_obs = obs[self.agent_index] # Compute non-normalized log probabilities from the underlying model - logits = self.policy.compute_actions( - np.array([my_obs]), self.rnn_state - )[2]["action_dist_inputs"] + logits = self.policy.compute_actions(np.array([my_obs]), self.rnn_state)[2][ + "action_dist_inputs" + ] # Softmax in numpy to convert logits to normalized probabilities return softmax(logits) @@ -170,9 +163,7 @@ def __init__( self.base_env = base_env # since we are not passing featurize_fn in as an argument, we create it here and check its validity self.featurize_fn_map = { - "ppo": lambda state: self.base_env.lossless_state_encoding_mdp( - state - ), + "ppo": lambda state: self.base_env.lossless_state_encoding_mdp(state), "bc": lambda state: self.base_env.featurize_state_mdp(state), } self._validate_featurize_fns(self.featurize_fn_map) @@ -188,31 +179,31 @@ def __init__( def _validate_featurize_fns(self, mapping): assert "ppo" in mapping, "At least one ppo agent must be specified" for k, v in mapping.items(): - assert ( - k in self.supported_agents - ), "Unsuported agent type in featurize mapping {0}".format(k) + assert k in self.supported_agents, ( + "Unsuported agent type in featurize mapping {0}".format(k) + ) assert callable(v), "Featurize_fn values must be functions" - assert ( - len(get_required_arguments(v)) == 1 - ), "Featurize_fn value must accept exactly one argument" + assert len(get_required_arguments(v)) == 1, ( + "Featurize_fn value must accept exactly one argument" + ) def _validate_schedule(self, schedule): timesteps = [p[0] for p in schedule] values = [p[1] for p in schedule] - assert ( - len(schedule) >= 2 - ), "Need at least 2 points to linearly interpolate schedule" + assert len(schedule) >= 2, ( + "Need at least 2 points to linearly interpolate schedule" + ) assert schedule[0][0] == 0, "Schedule must start at timestep 0" - assert all( - [t >= 0 for t in timesteps] - ), "All timesteps in schedule must be non-negative" - assert all( - [v >= 0 and v <= 1 for v in values] - ), "All values in schedule must be between 0 and 1" - assert ( - sorted(timesteps) == timesteps - ), "Timesteps must be in increasing order in schedule" + assert all([t >= 0 for t in timesteps]), ( + "All timesteps in schedule must be non-negative" + ) + assert all([v >= 0 and v <= 1 for v in values]), ( + "All values in schedule must be between 0 and 1" + ) + assert sorted(timesteps) == timesteps, ( + "Timesteps must be in increasing order in schedule" + ) # To ensure we flatline after passing last timestep if schedule[-1][0] < float("inf"): @@ -221,19 +212,15 @@ def _validate_schedule(self, schedule): def _setup_action_space(self, agents): action_sp = {} for agent in agents: - action_sp[agent] = gymnasium.spaces.Discrete( - len(Action.ALL_ACTIONS) - ) + action_sp[agent] = gymnasium.spaces.Discrete(len(Action.ALL_ACTIONS)) self.action_space = gymnasium.spaces.Dict(action_sp) - self.shared_action_space = gymnasium.spaces.Discrete( - len(Action.ALL_ACTIONS) - ) + self.shared_action_space = gymnasium.spaces.Discrete(len(Action.ALL_ACTIONS)) def _setup_observation_space(self, agents): dummy_state = self.base_env.mdp.get_standard_start_state() # ppo observation - featurize_fn_ppo = ( - lambda state: self.base_env.lossless_state_encoding_mdp(state) + featurize_fn_ppo = lambda state: self.base_env.lossless_state_encoding_mdp( + state ) obs_shape = featurize_fn_ppo(dummy_state)[0].shape @@ -244,9 +231,7 @@ def _setup_observation_space(self, agents): ) # bc observation - featurize_fn_bc = lambda state: self.base_env.featurize_state_mdp( - state - ) + featurize_fn_bc = lambda state: self.base_env.featurize_state_mdp(state) obs_shape = featurize_fn_bc(dummy_state)[0].shape high = np.ones(obs_shape) * 100 low = np.ones(obs_shape) * -100 @@ -264,9 +249,7 @@ def _setup_observation_space(self, agents): def _get_featurize_fn(self, agent_id): if agent_id.startswith("ppo"): - return lambda state: self.base_env.lossless_state_encoding_mdp( - state - ) + return lambda state: self.base_env.lossless_state_encoding_mdp(state) if agent_id.startswith("bc"): return lambda state: self.base_env.featurize_state_mdp(state) raise ValueError("Unsupported agent type {0}".format(agent_id)) @@ -342,12 +325,8 @@ def step(self, action_dict): ob_p0, ob_p1 = self._get_obs(next_state) - shaped_reward_p0 = ( - sparse_reward + self.reward_shaping_factor * dense_reward[0] - ) - shaped_reward_p1 = ( - sparse_reward + self.reward_shaping_factor * dense_reward[1] - ) + shaped_reward_p0 = sparse_reward + self.reward_shaping_factor * dense_reward[0] + shaped_reward_p1 = sparse_reward + self.reward_shaping_factor * dense_reward[1] obs = {self.curr_agents[0]: ob_p0, self.curr_agents[1]: ob_p1} rewards = { @@ -434,10 +413,9 @@ def from_config(cls, env_config): and "env_params" in env_config and "multi_agent_params" in env_config ) - assert ( - "mdp_params" in env_config - or "mdp_params_schedule_fn" in env_config - ), "either a fixed set of mdp params or a schedule function needs to be given" + assert "mdp_params" in env_config or "mdp_params_schedule_fn" in env_config, ( + "either a fixed set of mdp params or a schedule function needs to be given" + ) # "layout_name" and "rew_shaping_params" if "mdp_params" in env_config: mdp_params = env_config["mdp_params"] @@ -519,9 +497,7 @@ def on_train_result(self, trainer, result, **kwargs): # Anneal the bc factor based on environment paremeters and current timestep trainer.workers.foreach_worker( - lambda ev: ev.foreach_env( - lambda env: env.anneal_bc_factor(timestep) - ) + lambda ev: ev.foreach_env(lambda env: env.anneal_bc_factor(timestep)) ) def on_postprocess_trajectory( @@ -533,7 +509,7 @@ def on_postprocess_trajectory( policies, postprocessed_batch, original_batches, - **kwargs + **kwargs, ): pass @@ -685,9 +661,7 @@ def gen_trainer_from_params(params): "include_dashboard": False, "_temp_dir": params["ray_params"]["temp_dir"], "log_to_driver": params["verbose"], - "logging_level": logging.INFO - if params["verbose"] - else logging.CRITICAL, + "logging_level": logging.INFO if params["verbose"] else logging.CRITICAL, } ray.init(**init_params) register_env("overcooked_multi_agent", params["ray_params"]["env_creator"]) @@ -784,9 +758,7 @@ def select_policy(agent_id, episode, worker, **kwargs): environment_params["outer_shape"] = None if "mdp_params" in environment_params: - environment_params["eval_mdp_params"] = environment_params[ - "mdp_params" - ] + environment_params["eval_mdp_params"] = environment_params["mdp_params"] trainer = PPOTrainer( env="overcooked_multi_agent", config={ @@ -837,8 +809,7 @@ def load_trainer(save_path, true_num_workers=False): Returns a ray compatible trainer object that was previously saved at `save_path` by a call to `save_trainer` Note that `save_path` is the full path to the checkpoint directory Additionally we decide if we want to use the same number of remote workers (see ray library Training APIs) - as we store in the previous configuration, by default = False, we use only the local worker - (see ray library API) + as we store in the previous configuration, by default = False, we use only the local worker (see ray library API) """ # Read in params used to create trainer config_path = os.path.join(os.path.dirname(save_path), "config.pkl") @@ -855,14 +826,31 @@ def load_trainer(save_path, true_num_workers=False): if "trained_example" in save_path: # For the unit testing we update the result directory in order to avoid an error - config[ - "results_dir" - ] = "/Users/runner/work/human_aware_rl/human_aware_rl/human_aware_rl/ppo/results_temp" + config["results_dir"] = ( + "/Users/runner/work/human_aware_rl/human_aware_rl/human_aware_rl/ppo/results_temp" + ) # Get un-trained trainer object with proper config trainer = gen_trainer_from_params(config) + + # Search for the checkpoint folder in the save_path that has the highest number. Checkpoint folders are named checkpoint_ + checkpoint_dirs = [d for d in os.listdir(save_path) if d.startswith("checkpoint_")] + if not checkpoint_dirs: + raise ValueError(f"No checkpoint directories found in {save_path}") + + # Find directory with highest checkpoint number (2025: this was recently changed because previously checkpoints didn't have this format) + latest_dir = checkpoint_dirs[0] + latest_num = int(latest_dir.split("_")[1].lstrip("0") or "0") + for d in checkpoint_dirs[1:]: + num = int(d.split("_")[1].lstrip("0") or "0") + if num > latest_num: + latest_num = num + latest_dir = d + + checkpoint_file = os.path.join(save_path, latest_dir + "/") + # Load weights into dummy object - trainer.restore(save_path) + trainer.restore(checkpoint_file) return trainer @@ -901,6 +889,4 @@ def load_agent(save_path, policy_id="ppo", agent_index=0): as the featurization is not symmetric for both players """ trainer = load_trainer(save_path) - return get_agent_from_trainer( - trainer, policy_id=policy_id, agent_index=agent_index - ) + return get_agent_from_trainer(trainer, policy_id=policy_id, agent_index=agent_index) diff --git a/src/overcooked_ai_py/data/planners/__init__.py b/src/overcooked_ai_py/data/planners/__init__.py index 4e1dd800..5daffdb3 100644 --- a/src/overcooked_ai_py/data/planners/__init__.py +++ b/src/overcooked_ai_py/data/planners/__init__.py @@ -2,7 +2,6 @@ import pickle from overcooked_ai_py.static import PLANNERS_DIR -from overcooked_ai_py.utils import load_dict_from_file def load_saved_action_manager(filename): diff --git a/src/overcooked_ai_py/visualization/state_visualizer.py b/src/overcooked_ai_py/visualization/state_visualizer.py index 34e5cd0e..4b2d1b9a 100644 --- a/src/overcooked_ai_py/visualization/state_visualizer.py +++ b/src/overcooked_ai_py/visualization/state_visualizer.py @@ -16,7 +16,6 @@ ) from overcooked_ai_py.static import FONTS_DIR, GRAPHICS_DIR from overcooked_ai_py.utils import ( - classproperty, cumulative_rewards_from_rew_list, generate_temporary_file_path, ) @@ -53,9 +52,7 @@ class StateVisualizer: os.path.join(GRAPHICS_DIR, "chefs.json"), ) ARROW_IMG = pygame.image.load(os.path.join(GRAPHICS_DIR, "arrow.png")) - INTERACT_IMG = pygame.image.load( - os.path.join(GRAPHICS_DIR, "interact.png") - ) + INTERACT_IMG = pygame.image.load(os.path.join(GRAPHICS_DIR, "interact.png")) STAY_IMG = pygame.image.load(os.path.join(GRAPHICS_DIR, "stay.png")) UNSCALED_TILE_SIZE = 15 DEFAULT_VALUES = { @@ -159,9 +156,7 @@ def default_hud_data_from_trajectories(trajectories, trajectory_idx=0): ) return [ StateVisualizer.default_hud_data(state, score=scores[i]) - for i, state in enumerate( - trajectories["ep_states"][trajectory_idx] - ) + for i, state in enumerate(trajectories["ep_states"][trajectory_idx]) ] def display_rendered_trajectory( @@ -243,12 +238,10 @@ def display_rendered_state( window_display (bool): if True render state into pygame window action_probs(list(list(float))): action probs for every player acessed in the way action_probs[player][action] """ - assert ( - window_display or img_path or ipython_display - ), "specify at least one of the ways to output result state image: window_display, img_path, or ipython_display" - surface = self.render_state( - state, grid, hud_data, action_probs=action_probs + assert window_display or img_path or ipython_display, ( + "specify at least one of the ways to output result state image: window_display, img_path, or ipython_display" ) + surface = self.render_state(state, grid, hud_data, action_probs=action_probs) if img_path is None and ipython_display: img_path = generate_temporary_file_path( @@ -274,17 +267,13 @@ def render_state(self, state, grid, hud_data=None, action_probs=None): pygame.init() grid = grid or self.grid assert grid - grid_surface = pygame.surface.Surface( - self._unscaled_grid_pixel_size(grid) - ) + grid_surface = pygame.surface.Surface(self._unscaled_grid_pixel_size(grid)) self._render_grid(grid_surface, grid) self._render_players(grid_surface, state.players) self._render_objects(grid_surface, state.objects, grid) if self.scale_by_factor != 1: - grid_surface = scale_surface_by_factor( - grid_surface, self.scale_by_factor - ) + grid_surface = scale_surface_by_factor(grid_surface, self.scale_by_factor) # render text after rescaling as text looks bad when is rendered small resolution and then rescalled to bigger one if self.is_rendering_cooking_timer: @@ -292,9 +281,7 @@ def render_state(self, state, grid, hud_data=None, action_probs=None): # arrows does not seem good when rendered in very small resolution if self.is_rendering_action_probs and action_probs is not None: - self._render_actions_probs( - grid_surface, state.players, action_probs - ) + self._render_actions_probs(grid_surface, state.players, action_probs) if self.is_rendering_hud and hud_data: hud_width = self.width or grid_surface.get_width() @@ -336,9 +323,7 @@ def hud_line_height(self): @staticmethod def _check_config_validity(config): - assert set(config.keys()).issubset( - set(StateVisualizer.DEFAULT_VALUES.keys()) - ) + assert set(config.keys()).issubset(set(StateVisualizer.DEFAULT_VALUES.keys())) def _init_font(self, font_size, font_path=None, system_font_name=None): if system_font_name: @@ -348,9 +333,7 @@ def _init_font(self, font_size, font_path=None, system_font_name=None): ) else: key = "%i-path:%s" % (font_size, font_path) - font = self._fonts.get(key) or pygame.font.Font( - font_path, font_size - ) + font = self._fonts.get(key) or pygame.font.Font(font_path, font_size) self._fonts[key] = font return font @@ -442,9 +425,7 @@ def render_soup(surface, obj, grid): soup_status = "idle" else: # grid[x][y] != POT soup_status = "done" - frame_name = StateVisualizer._soup_frame_name( - obj.ingredients, soup_status - ) + frame_name = StateVisualizer._soup_frame_name(obj.ingredients, soup_status) self.SOUPS_IMG.blit_on_surface( surface, self._position_in_unscaled_pixels(obj.position), @@ -466,8 +447,7 @@ def _render_cooking_timers(self, surface, objects, grid): (x_pos, y_pos) = obj.position if obj.name == "soup" and grid[y_pos][x_pos] == POT: if obj._cooking_tick != -1 and ( - obj._cooking_tick <= obj.cook_time - or self.show_timer_when_cooked + obj._cooking_tick <= obj.cook_time or self.show_timer_when_cooked ): text_surface = self.cooking_timer_font.render( str(obj._cooking_tick), @@ -481,13 +461,9 @@ def _render_cooking_timers(self, surface, objects, grid): # calculate font position to be in center on x axis, and 0.9 from top on y axis font_position = ( tile_pos_x - + int( - (self.tile_size - text_surface.get_width()) * 0.5 - ), + + int((self.tile_size - text_surface.get_width()) * 0.5), tile_pos_y - + int( - (self.tile_size - text_surface.get_height()) * 0.9 - ), + + int((self.tile_size - text_surface.get_height()) * 0.9), ) surface.blit(text_surface, font_position) @@ -538,9 +514,7 @@ def get_hud_recipes_surface(orders_dicts): frame_name = StateVisualizer._soup_frame_name( order_dict["ingredients"], "done" ) - unscaled_order_surface = pygame.surface.Surface( - unscaled_order_size - ) + unscaled_order_surface = pygame.surface.Surface(unscaled_order_size) unscaled_order_surface.fill(self.background_color) self.SOUPS_IMG.blit_on_surface( unscaled_order_surface, (0, 0), frame_name @@ -552,14 +526,10 @@ def get_hud_recipes_surface(orders_dicts): unscaled_order_surface, (order_width, order_width) ) recipes_surface.blit(scaled_order_surface, (next_surface_x, 0)) - next_surface_x += ( - order_width + self.hud_distance_between_orders - ) + next_surface_x += order_width + self.hud_distance_between_orders return recipes_surface - for hud_line_num, (key, value) in enumerate( - self._sorted_hud_items(hud_data) - ): + for hud_line_num, (key, value) in enumerate(self._sorted_hud_items(hud_data)): hud_text = self._key_to_hud_text(key) if key not in [ "all_orders", @@ -569,9 +539,7 @@ def get_hud_recipes_surface(orders_dicts): ]: hud_text += str(value) - text_surface = self.hud_font.render( - hud_text, True, self.hud_font_color - ) + text_surface = self.hud_font.render(hud_text, True, self.hud_font_color) text_surface_position = hud_text_position(hud_line_num) surface.blit(text_surface, text_surface_position) @@ -681,9 +649,7 @@ def _render_actions_probs(self, surface, players, action_probs): # use math sqrt to make probability proportional to area of the image size = math.sqrt(probs[Action.ACTION_TO_INDEX[action]]) if action == "interact": - img = pygame.transform.rotozoom( - rescaled_interact, 0, size - ) + img = pygame.transform.rotozoom(rescaled_interact, 0, size) self._render_on_tile_position( surface, img, @@ -701,15 +667,10 @@ def _render_actions_probs(self, surface, players, action_probs): vertical_align="center", ) else: - position = Action.move_in_direction( - player.position, action - ) + position = Action.move_in_direction(player.position, action) img = pygame.transform.rotozoom( rescaled_arrow, direction_to_rotation[action], size ) self._render_on_tile_position( - surface, - img, - position, - **direction_to_aligns[action] + surface, img, position, **direction_to_aligns[action] ) diff --git a/src/overcooked_ai_py/visualization/visualization_utils.py b/src/overcooked_ai_py/visualization/visualization_utils.py index 4e37e0cc..980b05f9 100644 --- a/src/overcooked_ai_py/visualization/visualization_utils.py +++ b/src/overcooked_ai_py/visualization/visualization_utils.py @@ -12,27 +12,17 @@ def display_f(**kwargs): return interactive( display_f, - **{ - slider_label: IntSlider( - min=0, max=len(image_pathes_list) - 1, step=1 - ) - } + **{slider_label: IntSlider(min=0, max=len(image_pathes_list) - 1, step=1)}, ) -def show_ipython_images_slider( - image_pathes_list, slider_label="", first_arg=0 -): +def show_ipython_images_slider(image_pathes_list, slider_label="", first_arg=0): def display_f(**kwargs): display(Image(image_pathes_list[kwargs[slider_label]])) display( interactive( display_f, - **{ - slider_label: IntSlider( - min=0, max=len(image_pathes_list) - 1, step=1 - ) - } + **{slider_label: IntSlider(min=0, max=len(image_pathes_list) - 1, step=1)}, ) ) diff --git a/testing/overcooked_test.py b/testing/overcooked_test.py index 5f17a026..834cf870 100644 --- a/testing/overcooked_test.py +++ b/testing/overcooked_test.py @@ -1,4 +1,3 @@ -import copy import glob import json import os @@ -105,18 +104,14 @@ def test_caching(self): self.assertIs(self.r1, self.r2) self.assertIs(self.r3, self.r4) self.assertIs(self.r4, self.r5) - self.assertFalse( - self.r6 is self.r1, "different recipes cached to same value" - ) + self.assertFalse(self.r6 is self.r1, "different recipes cached to same value") def test_serialization(self): loaded_recipes = [] # Save and then load every recipe instance for i, recipe in enumerate(self.recipes): - pickle_path = os.path.join( - self.pickle_temp_dir, "recipe_{}".format(i) - ) + pickle_path = os.path.join(self.pickle_temp_dir, "recipe_{}".format(i)) save_pickle(recipe, pickle_path) loaded = load_pickle(pickle_path) loaded_recipes.append(loaded) @@ -154,9 +149,7 @@ def test_all_recipes(self): ) def test_invalid_input(self): - self.assertRaises( - ValueError, Recipe, [Recipe.ONION, Recipe.TOMATO, "carrot"] - ) + self.assertRaises(ValueError, Recipe, [Recipe.ONION, Recipe.TOMATO, "carrot"]) self.assertRaises(ValueError, Recipe, [Recipe.ONION] * 4) self.assertRaises(ValueError, Recipe, []) self.assertRaises(ValueError, Recipe, "invalid argument") @@ -167,9 +160,7 @@ def test_recipes_generation(self): Recipe.generate_random_recipes, max_size=Recipe.MAX_NUM_INGREDIENTS + 1, ) - self.assertRaises( - AssertionError, Recipe.generate_random_recipes, min_size=0 - ) + self.assertRaises(AssertionError, Recipe.generate_random_recipes, min_size=0) self.assertRaises( AssertionError, Recipe.generate_random_recipes, @@ -181,13 +172,9 @@ def test_recipes_generation(self): Recipe.generate_random_recipes, ingredients=["onion", "tomato", "fake_ingredient"], ) - self.assertRaises( - AssertionError, Recipe.generate_random_recipes, n=99999 - ) + self.assertRaises(AssertionError, Recipe.generate_random_recipes, n=99999) self.assertEqual(len(Recipe.generate_random_recipes(n=3)), 3) - self.assertEqual( - len(Recipe.generate_random_recipes(n=99, unique=False)), 99 - ) + self.assertEqual(len(Recipe.generate_random_recipes(n=99, unique=False)), 99) two_sized_recipes = [ Recipe(["onion", "onion"]), @@ -221,9 +208,7 @@ def test_recipes_generation(self): only_onions_recipes, set( [ - Recipe.generate_random_recipes( - n=1, recipes=only_onions_recipes - )[0] + Recipe.generate_random_recipes(n=1, recipes=only_onions_recipes)[0] for _ in range(100) ] ), @@ -305,9 +290,7 @@ def test_attributes(self): self.s2.ingredients, [Recipe.ONION, Recipe.ONION, Recipe.TOMATO] ) self.assertListEqual(self.s3.ingredients, [Recipe.ONION]) - self.assertListEqual( - self.s4.ingredients, [Recipe.TOMATO, Recipe.TOMATO] - ) + self.assertListEqual(self.s4.ingredients, [Recipe.TOMATO, Recipe.TOMATO]) try: self.s1.recipe @@ -315,9 +298,7 @@ def test_attributes(self): except ValueError as e: pass except Exception as e: - self.fail( - "Expected ValueError to be raised, {} raised instead".format(e) - ) + self.fail("Expected ValueError to be raised, {} raised instead".format(e)) try: self.s2.recipe @@ -325,13 +306,9 @@ def test_attributes(self): except ValueError as e: pass except Exception as e: - self.fail( - "Expected ValueError to be raised, {} raised instead".format(e) - ) + self.fail("Expected ValueError to be raised, {} raised instead".format(e)) self.assertEqual(self.s3.recipe, Recipe([Recipe.ONION])) - self.assertEqual( - self.s4.recipe, Recipe([Recipe.TOMATO, Recipe.TOMATO]) - ) + self.assertEqual(self.s4.recipe, Recipe([Recipe.TOMATO, Recipe.TOMATO])) def test_invalid_ops(self): # Cannot cook an empty soup @@ -350,14 +327,10 @@ def test_invalid_ops(self): self.assertRaises(ValueError, self.s4.begin_cooking) # Cannot add ingredients to a soup that is cooking - self.assertRaises( - ValueError, self.s3.add_ingredient_from_str, Recipe.ONION - ) + self.assertRaises(ValueError, self.s3.add_ingredient_from_str, Recipe.ONION) # Cannot add ingredients to a soup that is ready - self.assertRaises( - ValueError, self.s4.add_ingredient_from_str, Recipe.ONION - ) + self.assertRaises(ValueError, self.s4.add_ingredient_from_str, Recipe.ONION) # Cannot remove an ingredient from a soup that is ready self.assertRaises(ValueError, self.s4.pop_ingredient) @@ -408,27 +381,19 @@ def test_constructor_invalid_inputs(self): with self.assertRaises(AssertionError): # The grid can't be ragged. - mdp = OvercookedGridworld.from_grid( - ["XXPXX", "O 2XX", "X1 3 X", "XDXSXX"] - ) + mdp = OvercookedGridworld.from_grid(["XXPXX", "O 2XX", "X1 3 X", "XDXSXX"]) with self.assertRaises(AssertionError): # The agents must be numbered 1 and 2. - mdp = OvercookedGridworld.from_grid( - ["XXPXX", "O 3O", "X1 X", "XDXSX"] - ) + mdp = OvercookedGridworld.from_grid(["XXPXX", "O 3O", "X1 X", "XDXSX"]) with self.assertRaises(AssertionError): # The agents must be numbered 1 and 2. - mdp = OvercookedGridworld.from_grid( - ["XXPXX", "O 1O", "X1 X", "XDXSX"] - ) + mdp = OvercookedGridworld.from_grid(["XXPXX", "O 1O", "X1 X", "XDXSX"]) with self.assertRaises(AssertionError): # B is not a valid element. - mdp = OvercookedGridworld.from_grid( - ["XBPXX", "O 2O", "X1 X", "XDXSX"] - ) + mdp = OvercookedGridworld.from_grid(["XBPXX", "O 2O", "X1 X", "XDXSX"]) def test_start_positions(self): actual_start_state = self.base_mdp.get_standard_start_state() @@ -477,9 +442,7 @@ def test_actions(self): self.base_mdp.get_actions(bad_state) self.assertEqual( - self.base_mdp.get_actions( - self.base_mdp.get_standard_start_state() - ), + self.base_mdp.get_actions(self.base_mdp.get_standard_start_state()), [Action.ALL_ACTIONS, Action.ALL_ACTIONS], ) @@ -551,9 +514,7 @@ def check_transition(action, expected_path, recompute=False): check_transition([n, e], expected_path, recompute=False) def test_mdp_dynamics(self): - traj_path = os.path.join( - TESTING_DATA_DIR, "test_mdp_dynamics", "expected.json" - ) + traj_path = os.path.join(TESTING_DATA_DIR, "test_mdp_dynamics", "expected.json") # NOTE: uncomment the following line to recompute trajectories if MDP dymamics were deliberately updated # generate_serialized_trajectory(self.base_mdp, traj_path) @@ -577,15 +538,11 @@ def test_mdp_old_cook_dynamics(self): layout_name="old_dynamics_cook_test", old_dynamics=False ) # test interacting with a 1-ingredient soup starts cooking in the new dynamics - new_state_n, _ = new_mdp.get_state_transition( - new_mdp.start_state, [interact] - ) + new_state_n, _ = new_mdp.get_state_transition(new_mdp.start_state, [interact]) soup_new = new_state_n.get_object((2, 0)) self.assertTrue(soup_new.is_cooking) # this should have no effects - new_state_o, _ = old_mdp.get_state_transition( - old_mdp.start_state, [interact] - ) + new_state_o, _ = old_mdp.get_state_transition(old_mdp.start_state, [interact]) soup_old = new_state_o.get_object((2, 0)) self.assertFalse(soup_old.is_cooking) @@ -597,15 +554,11 @@ def test_mdp_old_put_dynamics(self): layout_name="old_dynamics_put_test", old_dynamics=False ) # putting in the third ingredient will not start cooking in the new dynamics - new_state_n, _ = new_mdp.get_state_transition( - new_mdp.start_state, [interact] - ) + new_state_n, _ = new_mdp.get_state_transition(new_mdp.start_state, [interact]) soup_new = new_state_n.get_object((2, 0)) self.assertFalse(soup_new.is_cooking) # cooking should start automatically in the old dynamics - new_state_o, _ = old_mdp.get_state_transition( - old_mdp.start_state, [interact] - ) + new_state_o, _ = old_mdp.get_state_transition(old_mdp.start_state, [interact]) soup_old = new_state_o.get_object((2, 0)) self.assertTrue(soup_old.is_cooking) @@ -641,9 +594,7 @@ def test_mdp_serialization(self): # Advance state joint_action, _ = zip(*random_pair.joint_action(state)) - state, infos = self.base_mdp.get_state_transition( - state, joint_action - ) + state, infos = self.base_mdp.get_state_transition(state, joint_action) sparse_reward += sum(infos["sparse_reward_by_agent"]) seed += 1 @@ -665,14 +616,10 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) actions = [Direction.EAST, Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [Action.STAY, action] - ) + state, _ = self.base_mdp.get_state_transition(state, [Action.STAY, action]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val1 = self.base_mdp.potential_function(state, mp) # Pick up tomato @@ -680,22 +627,14 @@ def test_potential_function(self): print("pick up tomtato") actions = [Direction.WEST, Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val2 = self.base_mdp.potential_function(state, mp) - self.assertLess( - val0, val1, "Picking up onion should increase potential" - ) - self.assertLess( - val1, val2, "Picking up tomato should increase potential" - ) + self.assertLess(val0, val1, "Picking up onion should increase potential") + self.assertLess(val1, val2, "Picking up tomato should increase potential") # Pot tomato if self.verbose: @@ -707,14 +646,10 @@ def test_potential_function(self): Direction.WEST, ] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val3 = self.base_mdp.potential_function(state, mp) # Pot onion @@ -722,14 +657,10 @@ def test_potential_function(self): print("pot onion") actions = [Direction.WEST, Direction.NORTH, Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [Action.STAY, action] - ) + state, _ = self.base_mdp.get_state_transition(state, [Action.STAY, action]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val4 = self.base_mdp.potential_function(state, mp) self.assertLess(val2, val3, "Potting tomato should increase potential") @@ -753,22 +684,14 @@ def test_potential_function(self): print("pick up tomato") actions = [Direction.SOUTH, Direction.EAST, Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [Action.STAY, action] - ) + state, _ = self.base_mdp.get_state_transition(state, [Action.STAY, action]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val6 = self.base_mdp.potential_function(state, mp) - self.assertLess( - val4, val5, "Picking up onion should increase potential" - ) - self.assertLess( - val5, val6, "Picking up tomato should increase potential" - ) + self.assertLess(val4, val5, "Picking up onion should increase potential") + self.assertLess(val5, val6, "Picking up tomato should increase potential") # Pot onion if self.verbose: @@ -781,14 +704,10 @@ def test_potential_function(self): Direction.WEST, ] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val7 = self.base_mdp.potential_function(state, mp) # Pot tomato @@ -796,14 +715,10 @@ def test_potential_function(self): print("pot tomato") actions = [Direction.WEST, Direction.SOUTH, Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [Action.STAY, action] - ) + state, _ = self.base_mdp.get_state_transition(state, [Action.STAY, action]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val8 = self.base_mdp.potential_function(state, mp) self.assertLess(val6, val7, "Potting onion should increase potential") @@ -816,14 +731,10 @@ def test_potential_function(self): print("pickup tomato") actions = [Action.INTERACT, Direction.NORTH] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val9 = self.base_mdp.potential_function(state, mp) # pickup tomato @@ -831,14 +742,10 @@ def test_potential_function(self): print("pickup tomato") actions = [Direction.EAST, Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [Action.STAY, action] - ) + state, _ = self.base_mdp.get_state_transition(state, [Action.STAY, action]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val10 = self.base_mdp.potential_function(state, mp) self.assertLessEqual( @@ -855,19 +762,13 @@ def test_potential_function(self): print("pot catastrophic tomato") actions = [Direction.WEST, Direction.SOUTH, Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [Action.STAY, action] - ) + state, _ = self.base_mdp.get_state_transition(state, [Action.STAY, action]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val11 = self.base_mdp.potential_function(state, mp) - self.assertLess( - val11, val10, "Catastrophic potting should decrease potential" - ) + self.assertLess(val11, val10, "Catastrophic potting should decrease potential") ## Bonus soup creation @@ -881,14 +782,10 @@ def test_potential_function(self): Action.INTERACT, ] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val12 = self.base_mdp.potential_function(state, mp) # pot onion @@ -896,14 +793,10 @@ def test_potential_function(self): print("pot onion") actions = [Direction.EAST, Direction.NORTH, Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val13 = self.base_mdp.potential_function(state, mp) # Cook soup @@ -911,25 +804,15 @@ def test_potential_function(self): print("cook soup") actions = [Action.INTERACT, Direction.WEST] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val14 = self.base_mdp.potential_function(state, mp) - self.assertLess( - val11, val12, "Useful onion pickup should increase potential" - ) - self.assertLess( - val12, val13, "Potting useful onion should increase potential" - ) - self.assertLess( - val13, val14, "Cooking optimal soup should increase potential" - ) + self.assertLess(val11, val12, "Useful onion pickup should increase potential") + self.assertLess(val12, val13, "Potting useful onion should increase potential") + self.assertLess(val13, val14, "Cooking optimal soup should increase potential") ## Soup pickup ## @@ -938,14 +821,10 @@ def test_potential_function(self): print("pick up dish") actions = [Direction.WEST, Direction.SOUTH, Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [Action.STAY, action] - ) + state, _ = self.base_mdp.get_state_transition(state, [Action.STAY, action]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val15 = self.base_mdp.potential_function(state, mp) # Move towards pot @@ -953,14 +832,10 @@ def test_potential_function(self): print("move towards pot") actions = [Direction.EAST, Direction.NORTH] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [Action.STAY, action] - ) + state, _ = self.base_mdp.get_state_transition(state, [Action.STAY, action]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val16 = self.base_mdp.potential_function(state, mp) # Pickup soup @@ -974,17 +849,13 @@ def test_potential_function(self): print("potential: ", self.base_mdp.potential_function(state, mp)) val17 = self.base_mdp.potential_function(state, mp) - self.assertLess( - val14, val15, "Useful dish pickups should increase potential" - ) + self.assertLess(val14, val15, "Useful dish pickups should increase potential") self.assertLess( val15, val16, "Moving towards soup with dish should increase potential", ) - self.assertLess( - val16, val17, "Picking up soup should increase potential" - ) + self.assertLess(val16, val17, "Picking up soup should increase potential") ## Removing failed soup from pot @@ -993,27 +864,19 @@ def test_potential_function(self): print("move torwards failed soup") actions = [Direction.SOUTH, Direction.EAST, Direction.SOUTH] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val18 = self.base_mdp.potential_function(state, mp) # Cook failed soup actions = [Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val19 = self.base_mdp.potential_function(state, mp) # Pickup dish @@ -1021,14 +884,10 @@ def test_potential_function(self): print("pickup dish") actions = [Direction.WEST, Direction.SOUTH, Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val20 = self.base_mdp.potential_function(state, mp) # Move towards soup @@ -1036,14 +895,10 @@ def test_potential_function(self): print("move towards soup") actions = [Direction.EAST, Direction.SOUTH] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val21 = self.base_mdp.potential_function(state, mp) self.assertLess( @@ -1051,12 +906,8 @@ def test_potential_function(self): val18, "Moving towards failed soup should increase potential", ) - self.assertLess( - val18, val19, "Cooking failed soup should increase potential" - ) - self.assertLess( - val19, val20, "Dish pickup for failed soup is still useful" - ) + self.assertLess(val18, val19, "Cooking failed soup should increase potential") + self.assertLess(val19, val20, "Dish pickup for failed soup is still useful") self.assertLess( val20, val21, @@ -1068,14 +919,10 @@ def test_potential_function(self): # Pickup failed soup actions = [Action.INTERACT] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val22 = self.base_mdp.potential_function(state, mp) # Move towards serving area @@ -1083,14 +930,10 @@ def test_potential_function(self): print("move towards servering area") actions = [Direction.EAST, Direction.SOUTH] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [action, Action.STAY] - ) + state, _ = self.base_mdp.get_state_transition(state, [action, Action.STAY]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val23 = self.base_mdp.potential_function(state, mp) # Move away from serving area @@ -1127,14 +970,10 @@ def test_potential_function(self): print("move towards serving area") actions = [Direction.SOUTH, Direction.EAST, Direction.SOUTH] for action in actions: - state, _ = self.base_mdp.get_state_transition( - state, [Action.STAY, action] - ) + state, _ = self.base_mdp.get_state_transition(state, [Action.STAY, action]) if self.verbose: print(self.base_mdp.state_string(state)) - print( - "potential: ", self.base_mdp.potential_function(state, mp) - ) + print("potential: ", self.base_mdp.potential_function(state, mp)) val25 = self.base_mdp.potential_function(state, mp) # Deliver soup @@ -1198,9 +1037,7 @@ def test_state_featurization_shape(self): obs_0, obs_1 = self.base_mdp.featurize_state( s, self.mlam, num_pots=num_pots ) - expected_shape = self.base_mdp.get_featurize_state_shape( - num_pots=num_pots - ) + expected_shape = self.base_mdp.get_featurize_state_shape(num_pots=num_pots) self.assertTrue( np.array_equal(obs_0.shape, expected_shape), "{} vs {}".format(obs_0.shape, expected_shape), @@ -1215,10 +1052,7 @@ def test_lossless_state_featurization(self): self.greedy_human_model_pair, num_games=5, info=False ) featurized_observations = [ - [ - self.base_mdp.lossless_state_encoding(state) - for state in ep_states - ] + [self.base_mdp.lossless_state_encoding(state) for state in ep_states] for ep_states in trajs["ep_states"] ] @@ -1230,9 +1064,7 @@ def test_lossless_state_featurization(self): # featurizations by uncommenting the following line: # save_pickle(featurized_observations, pickle_path) expected_featurization = load_pickle(pickle_path) - self.assertTrue( - np.array_equal(expected_featurization, featurized_observations) - ) + self.assertTrue(np.array_equal(expected_featurization, featurized_observations)) def test_state_featurization(self): trajs = self.env.get_rollouts( @@ -1242,9 +1074,7 @@ def test_state_featurization(self): for num_pots in range(3): featurized_observations = [ [ - self.base_mdp.featurize_state( - state, self.mlam, num_pots=num_pots - ) + self.base_mdp.featurize_state(state, self.mlam, num_pots=num_pots) for state in ep_states ] for ep_states in trajs["ep_states"] @@ -1266,14 +1096,10 @@ def test_state_featurization_symmetry(self): trajs = self.env.get_rollouts( self.greedy_human_model_pair, num_games=5, info=False ) - states = [ - state for ep_states in trajs["ep_states"] for state in ep_states - ] + states = [state for ep_states in trajs["ep_states"] for state in ep_states] for state in states: - p0_obs, p1_obs = self.base_mdp.featurize_state( - state, self.mlam, num_pots=2 - ) + p0_obs, p1_obs = self.base_mdp.featurize_state(state, self.mlam, num_pots=2) state = state.reverse_players() p0_obs_swapped, p1_obs_swapped = self.base_mdp.featurize_state( state, self.mlam, num_pots=2 @@ -1287,9 +1113,7 @@ def test_lossless_state_featurization_symmetry(self): trajs = self.env.get_rollouts( self.greedy_human_model_pair, num_games=5, info=False ) - states = [ - state for ep_states in trajs["ep_states"] for state in ep_states - ] + states = [state for ep_states in trajs["ep_states"] for state in ep_states] for state in states: p0_obs, p1_obs = self.base_mdp.lossless_state_encoding(state) @@ -1338,9 +1162,7 @@ def test_constructor(self): self.fail("Failed to instantiate OvercookedEnv:\n{}".format(e)) with self.assertRaises(TypeError): - OvercookedEnv.from_mdp( - self.base_mdp, **{"invalid_env_param": None} - ) + OvercookedEnv.from_mdp(self.base_mdp, **{"invalid_env_param": None}) def test_step_fn(self): for _ in range(10): @@ -1349,9 +1171,7 @@ def test_step_fn(self): def test_execute_plan(self): action_plan = [random_joint_action() for _ in range(10)] - self.env.execute_plan( - self.base_mdp.get_standard_start_state(), action_plan - ) + self.env.execute_plan(self.base_mdp.get_standard_start_state(), action_plan) def test_run_agents(self): start_state = self.env.state @@ -1399,9 +1219,7 @@ def test_four_player_env_fixed(self): e, ] ) - a2 = FixedPlanAgent( - [stay, w, interact, n, n, e, e, e, n, e, n, interact, w] - ) + a2 = FixedPlanAgent([stay, w, interact, n, n, e, e, e, n, e, n, interact, w]) a3 = FixedPlanAgent([e, interact, n, n, w, w, w, n, interact, e, s]) ag = AgentGroup(a0, a1, a2, a3) env.run_agents(ag, display=False) @@ -1430,9 +1248,7 @@ def test_display(self): expected_display_file = os.path.join( TESTING_DATA_DIR, "test_display", "expected.txt" ) - actual_display_file = glob.glob(os.path.join(self.dummy_dir, "*.txt"))[ - 0 - ] + actual_display_file = glob.glob(os.path.join(self.dummy_dir, "*.txt"))[0] # If display intentionally updated, uncomment the line below to update expected values shutil.copy(actual_display_file, expected_display_file) @@ -1454,9 +1270,7 @@ def test_display_phi(self): expected_display_file = os.path.join( TESTING_DATA_DIR, "test_display_phi", "expected.txt" ) - actual_display_file = glob.glob(os.path.join(self.dummy_dir, "*.txt"))[ - 0 - ] + actual_display_file = glob.glob(os.path.join(self.dummy_dir, "*.txt"))[0] # If display intentionally updated, uncomment the line below to update expected values shutil.copy(actual_display_file, expected_display_file) @@ -1476,9 +1290,7 @@ def test_starting_position_randomization(self): start_state_fn = self.base_mdp.get_random_start_state_fn( random_start_pos=True, rnd_obj_prob_thresh=0.0 ) - env = OvercookedEnv.from_mdp( - self.base_mdp, start_state_fn, info_level=0 - ) + env = OvercookedEnv.from_mdp(self.base_mdp, start_state_fn, info_level=0) start_state = env.state.players_pos_and_or for _ in range(3): env.reset() @@ -1490,9 +1302,7 @@ def test_starting_obj_randomization(self): start_state_fn = self.base_mdp.get_random_start_state_fn( random_start_pos=False, rnd_obj_prob_thresh=0.8 ) - env = OvercookedEnv.from_mdp( - self.base_mdp, start_state_fn, info_level=0 - ) + env = OvercookedEnv.from_mdp(self.base_mdp, start_state_fn, info_level=0) start_state = env.state.all_objects_list for _ in range(3): env.reset() @@ -1536,10 +1346,7 @@ def test_random_layout(self): env.reset() all_same_layout = all( - [ - np.array_equal(env.mdp.terrain_mtx, terrain) - for terrain in layouts_seen - ] + [np.array_equal(env.mdp.terrain_mtx, terrain) for terrain in layouts_seen] ) self.assertTrue(all_same_layout) @@ -1551,10 +1358,7 @@ def test_random_layout(self): env.reset() all_same_layout = all( - [ - np.array_equal(env.mdp.terrain_mtx, terrain) - for terrain in layouts_seen - ] + [np.array_equal(env.mdp.terrain_mtx, terrain) for terrain in layouts_seen] ) self.assertFalse(all_same_layout) @@ -1568,9 +1372,7 @@ def test_random_layout_feature_types(self): ] for optional_features_combo in optional_features_combinations: - left_out_optional_features = ( - optional_features - optional_features_combo - ) + left_out_optional_features = optional_features - optional_features_combo used_features = list(optional_features_combo | mandatory_features) mdp_gen_params = { "prop_feats": 0.9, @@ -1578,9 +1380,7 @@ def test_random_layout_feature_types(self): "prop_empty": 0.1, "inner_shape": (6, 5), "display": False, - "start_all_orders": [ - {"ingredients": ["onion", "onion", "onion"]} - ], + "start_all_orders": [{"ingredients": ["onion", "onion", "onion"]}], } mdp_fn = LayoutGenerator.mdp_gen_fn_from_dict( mdp_gen_params, outer_shape=(6, 5) @@ -1589,9 +1389,7 @@ def test_random_layout_feature_types(self): for _ in range(10): env.reset() curr_terrain = env.mdp.terrain_mtx - terrain_features = set.union( - *(set(line) for line in curr_terrain) - ) + terrain_features = set.union(*(set(line) for line in curr_terrain)) self.assertTrue( all(elem in terrain_features for elem in used_features) ) # all used_features are actually used @@ -1629,9 +1427,7 @@ def test_random_layout_generated_recipes(self): env = OvercookedEnv(mdp_fn, **DEFAULT_ENV_PARAMS) for _ in range(10): env.reset() - self.assertCountEqual( - env.mdp.start_all_orders, only_onions_dict_recipes - ) + self.assertCountEqual(env.mdp.start_all_orders, only_onions_dict_recipes) self.assertEqual(len(env.mdp.start_bonus_orders), 0) # checking if bonus_orders is subset of all_orders even if not specified @@ -1655,13 +1451,9 @@ def test_random_layout_generated_recipes(self): env = OvercookedEnv(mdp_fn, **DEFAULT_ENV_PARAMS) for _ in range(10): env.reset() - self.assertCountEqual( - env.mdp.start_all_orders, only_onions_dict_recipes - ) + self.assertCountEqual(env.mdp.start_all_orders, only_onions_dict_recipes) self.assertEqual(len(env.mdp.start_bonus_orders), 1) - self.assertTrue( - env.mdp.start_bonus_orders[0] in only_onions_dict_recipes - ) + self.assertTrue(env.mdp.start_bonus_orders[0] in only_onions_dict_recipes) # checking if after reset there are new recipes generated mdp_gen_params = { diff --git a/uv.lock b/uv.lock index f1fb0c15..c56fe6b9 100644 --- a/uv.lock +++ b/uv.lock @@ -1,46 +1,19 @@ version = 1 -requires-python = ">=3.9" +requires-python = "==3.10.*" resolution-markers = [ - "python_full_version < '3.10' and platform_machine == 'arm64' and platform_system == 'Darwin' and sys_platform == 'linux'", - "python_full_version < '3.10' and platform_machine == 'arm64' and platform_system == 'Darwin' and sys_platform != 'linux'", - "python_full_version < '3.10' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", - "python_full_version < '3.10' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'linux'", - "python_full_version < '3.10' and platform_machine == 'x86_64' and sys_platform == 'darwin'", - "(python_full_version < '3.10' and platform_machine != 'aarch64' and platform_machine != 'arm64' and sys_platform == 'linux') or (python_full_version < '3.10' and platform_machine == 'aarch64' and platform_system != 'Linux' and sys_platform == 'linux') or (python_full_version < '3.10' and platform_machine == 'arm64' and platform_system != 'Darwin' and sys_platform == 'linux')", - "(python_full_version < '3.10' and platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform != 'linux') or (python_full_version < '3.10' and platform_machine == 'aarch64' and platform_system != 'Linux' and sys_platform != 'linux') or (python_full_version < '3.10' and platform_machine == 'arm64' and platform_system != 'Darwin' and sys_platform != 'linux') or (python_full_version < '3.10' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.10.*' and platform_machine == 'x86_64' and platform_system == 'Darwin' and sys_platform == 'darwin'", - "python_full_version == '3.10.*' and platform_system == 'Darwin' and sys_platform == 'linux'", - "(python_full_version == '3.10.*' and platform_machine != 'x86_64' and platform_system == 'Darwin' and sys_platform != 'linux') or (python_full_version == '3.10.*' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", - "python_full_version == '3.10.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'linux'", - "python_full_version == '3.10.*' and platform_machine == 'x86_64' and platform_system != 'Darwin' and sys_platform == 'darwin'", - "(python_full_version == '3.10.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'linux') or (python_full_version == '3.10.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux')", - "(python_full_version == '3.10.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and platform_system != 'Darwin' and sys_platform != 'linux') or (python_full_version == '3.10.*' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux') or (python_full_version == '3.10.*' and platform_machine == 'x86_64' and platform_system != 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.11.*' and platform_machine == 'x86_64' and platform_system == 'Darwin' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform == 'linux'", - "(python_full_version == '3.11.*' and platform_machine != 'x86_64' and platform_system == 'Darwin' and sys_platform != 'linux') or (python_full_version == '3.11.*' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'linux'", - "python_full_version == '3.11.*' and platform_machine == 'x86_64' and platform_system != 'Darwin' and sys_platform == 'darwin'", - "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'linux') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux')", - "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and platform_system != 'Darwin' and sys_platform != 'linux') or (python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux') or (python_full_version == '3.11.*' and platform_machine == 'x86_64' and platform_system != 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version >= '3.12' and platform_machine == 'x86_64' and platform_system == 'Darwin' and sys_platform == 'darwin'", - "python_full_version >= '3.12' and platform_system == 'Darwin' 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url = "https://files.pythonhosted.org/packages/78/58/e860788190eba3bcce367f74d29c4675466ce8dddfba85f7827588416f01/wsproto-1.2.0-py3-none-any.whl", hash = "sha256:b9acddd652b585d75b20477888c56642fdade28bdfd3579aa24a4d2c037dd736", size = 24226 }, -] - -[[package]] -name = "zipp" -version = "3.21.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/3f/50/bad581df71744867e9468ebd0bcd6505de3b275e06f202c2cb016e3ff56f/zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4", size = 24545 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/b7/1a/7e4798e9339adc931158c9d69ecc34f5e6791489d469f5e50ec15e35f458/zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931", size = 9630 }, -] From d56526ce854390570e409e2d7bb39cc578ea0643 Mon Sep 17 00:00:00 2001 From: micah Date: Sat, 22 Mar 2025 11:01:03 -0700 Subject: [PATCH 07/13] pre tf upgrade --- .../imitation/behavior_cloning_tf2.py | 109 ++++++------------ 1 file changed, 34 insertions(+), 75 deletions(-) diff --git a/src/human_aware_rl/imitation/behavior_cloning_tf2.py b/src/human_aware_rl/imitation/behavior_cloning_tf2.py index 54dd7517..25b99659 100644 --- a/src/human_aware_rl/imitation/behavior_cloning_tf2.py +++ b/src/human_aware_rl/imitation/behavior_cloning_tf2.py @@ -6,19 +6,11 @@ import tensorflow as tf from ray.rllib.policy import Policy as RllibPolicy from tensorflow import keras -from tensorflow.compat.v1.keras.backend import get_session, set_session +from tensorflow.compat.v1.keras.backend import get_session from human_aware_rl.data_dir import DATA_DIR -from human_aware_rl.human.process_dataframes import ( - get_human_human_trajectories, - get_trajs_from_data, -) -from human_aware_rl.rllib.rllib import ( - RlLibAgent, - evaluate, - get_base_ae, - softmax, -) +from human_aware_rl.human.process_dataframes import get_human_human_trajectories +from human_aware_rl.rllib.rllib import evaluate, get_base_ae, softmax from human_aware_rl.static import CLEAN_2019_HUMAN_DATA_TRAIN from human_aware_rl.utils import get_flattened_keys, recursive_dict_update from overcooked_ai_py.mdp.actions import Action @@ -119,9 +111,7 @@ def get_bc_params(**args_to_override): all_keys = get_flattened_keys(params) if len(all_keys) != len(set(all_keys)): - raise ValueError( - "Every key at every level must be distict for BC params!" - ) + raise ValueError("Every key at every level must be distict for BC params!") return params @@ -198,9 +188,7 @@ def train_bc_model(model_dir, bc_params, verbose=False): class_weights = None # Retrieve un-initialized keras model - model = build_bc_model( - **bc_params, max_seq_len=np.max(seq_lens), verbose=verbose - ) + model = build_bc_model(**bc_params, max_seq_len=np.max(seq_lens), verbose=verbose) # Initialize the model # Note: have to use lists for multi-output model support and not dicts because of tensorlfow 2.0.0 bug @@ -225,9 +213,7 @@ def train_bc_model(model_dir, bc_params, verbose=False): # Early terminate training if loss doesn't improve for "patience" epochs keras.callbacks.EarlyStopping(monitor="loss", patience=20), # Reduce lr by "factor" after "patience" epochs of no improvement in loss - keras.callbacks.ReduceLROnPlateau( - monitor="loss", patience=3, factor=0.1 - ), + keras.callbacks.ReduceLROnPlateau(monitor="loss", patience=3, factor=0.1), # Log all metrics model was compiled with to tensorboard every epoch keras.callbacks.TensorBoard( log_dir=os.path.join(model_dir, "logs"), write_graph=False @@ -329,12 +315,8 @@ def featurize_fn(state): return base_env.featurize_state_mdp(state) # Wrap Keras models in rllib policies - agent_0_policy = BehaviorCloningPolicy.from_model( - model, bc_params, stochastic=True - ) - agent_1_policy = BehaviorCloningPolicy.from_model( - model, bc_params, stochastic=True - ) + agent_0_policy = BehaviorCloningPolicy.from_model(model, bc_params, stochastic=True) + agent_1_policy = BehaviorCloningPolicy.from_model(model, bc_params, stochastic=True) # Compute the results of the rollout(s) results = evaluate( @@ -355,21 +337,17 @@ def featurize_fn(state): def _build_model(observation_shape, action_shape, mlp_params, **kwargs): ## Inputs - inputs = keras.Input( - shape=observation_shape, name="Overcooked_observation" - ) + inputs = keras.Input(shape=observation_shape, name="Overcooked_observation") x = inputs ## Build fully connected layers - assert ( - len(mlp_params["net_arch"]) == mlp_params["num_layers"] - ), "Invalid Fully Connected params" + assert len(mlp_params["net_arch"]) == mlp_params["num_layers"], ( + "Invalid Fully Connected params" + ) for i in range(mlp_params["num_layers"]): units = mlp_params["net_arch"][i] - x = keras.layers.Dense( - units, activation="relu", name="fc_{0}".format(i) - )(x) + x = keras.layers.Dense(units, activation="relu", name="fc_{0}".format(i))(x) ## output layer logits = keras.layers.Dense(action_shape[0], name="logits")(x) @@ -378,12 +356,7 @@ def _build_model(observation_shape, action_shape, mlp_params, **kwargs): def _build_lstm_model( - observation_shape, - action_shape, - mlp_params, - cell_size, - max_seq_len=20, - **kwargs + observation_shape, action_shape, mlp_params, cell_size, max_seq_len=20, **kwargs ): ## Inputs obs_in = keras.Input( @@ -395,21 +368,19 @@ def _build_lstm_model( x = obs_in ## Build fully connected layers - assert ( - len(mlp_params["net_arch"]) == mlp_params["num_layers"] - ), "Invalid Fully Connected params" + assert len(mlp_params["net_arch"]) == mlp_params["num_layers"], ( + "Invalid Fully Connected params" + ) for i in range(mlp_params["num_layers"]): units = mlp_params["net_arch"][i] x = keras.layers.TimeDistributed( - keras.layers.Dense( - units, activation="relu", name="fc_{0}".format(i) - ) + keras.layers.Dense(units, activation="relu", name="fc_{0}".format(i)) )(x) - mask = keras.layers.Lambda( - lambda x: tf.sequence_mask(x, maxlen=max_seq_len) - )(seq_in) + mask = keras.layers.Lambda(lambda x: tf.sequence_mask(x, maxlen=max_seq_len))( + seq_in + ) ## LSTM layer lstm_out, h_out, c_out = keras.layers.LSTM( @@ -488,17 +459,15 @@ def __init__(self, observation_space, action_space, config): ) if "bc_model" in config and config["bc_model"]: - assert ( - "bc_params" in config - ), "must specify params in addition to model" - assert issubclass( - type(config["bc_model"]), keras.Model - ), "model must be of type keras.Model" + assert "bc_params" in config, "must specify params in addition to model" + assert issubclass(type(config["bc_model"]), keras.Model), ( + "model must be of type keras.Model" + ) model, bc_params = config["bc_model"], config["bc_params"] else: - assert ( - "model_dir" in config - ), "must specify model directory if model not specified" + assert "model_dir" in config, ( + "must specify model directory if model not specified" + ) model, bc_params = load_bc_model(config["model_dir"]) # Save the session that the model was loaded into so it is available at inference time if necessary @@ -513,9 +482,7 @@ def __init__(self, observation_space, action_space, config): self.stochastic = config["stochastic"] self.use_lstm = bc_params["use_lstm"] self.cell_size = bc_params["cell_size"] - self.eager = ( - config["eager"] if "eager" in config else bc_params["eager"] - ) + self.eager = config["eager"] if "eager" in config else bc_params["eager"] self.context = self._create_execution_context() def _setup_shapes(self): @@ -540,9 +507,7 @@ def from_model_dir(cls, model_dir, stochastic=True): "bc_params": bc_params, "stochastic": stochastic, } - return cls( - bc_params["observation_shape"], bc_params["action_shape"], config - ) + return cls(bc_params["observation_shape"], bc_params["action_shape"], config) @classmethod def from_model(cls, model, bc_params, stochastic=True): @@ -551,9 +516,7 @@ def from_model(cls, model, bc_params, stochastic=True): "bc_params": bc_params, "stochastic": stochastic, } - return cls( - bc_params["observation_shape"], bc_params["action_shape"], config - ) + return cls(bc_params["observation_shape"], bc_params["action_shape"], config) def compute_actions( self, @@ -563,7 +526,7 @@ def compute_actions( prev_reward_batch=None, info_batch=None, episodes=None, - **kwargs + **kwargs, ): """ Computes sampled actions for each of the corresponding OvercookedEnv states in obs_batch @@ -641,9 +604,7 @@ def _forward(self, obs_batch, state_batches): if self.use_lstm: obs_batch = np.expand_dims(obs_batch, 1) seq_lens = np.ones(len(obs_batch)) - model_out = self.model.predict( - [obs_batch, seq_lens] + state_batches - ) + model_out = self.model.predict([obs_batch, seq_lens] + state_batches) logits, states = model_out[0], model_out[1:] logits = logits.reshape((logits.shape[0], -1)) return logits, states @@ -663,8 +624,6 @@ def _create_execution_context(self): if __name__ == "__main__": params = get_bc_params() - model = train_bc_model( - os.path.join(BC_SAVE_DIR, "default"), params, verbose=True - ) + model = train_bc_model(os.path.join(BC_SAVE_DIR, "default"), params, verbose=True) # Evaluate our model's performance in a rollout evaluate_bc_model(model, params) From 9f15bf00429a43ada3a253f76a7df575ae7739a7 Mon Sep 17 00:00:00 2001 From: micah Date: Sat, 22 Mar 2025 11:35:16 -0700 Subject: [PATCH 08/13] Giving up on RL training, at some point worked with rllib=2.2 --- .../imitation/behavior_cloning_tf2.py | 100 ++++++------------ .../imitation/behavior_cloning_tf2_test.py | 37 ++++--- 2 files changed, 54 insertions(+), 83 deletions(-) diff --git a/src/human_aware_rl/imitation/behavior_cloning_tf2.py b/src/human_aware_rl/imitation/behavior_cloning_tf2.py index 25b99659..5778dbc0 100644 --- a/src/human_aware_rl/imitation/behavior_cloning_tf2.py +++ b/src/human_aware_rl/imitation/behavior_cloning_tf2.py @@ -6,7 +6,6 @@ import tensorflow as tf from ray.rllib.policy import Policy as RllibPolicy from tensorflow import keras -from tensorflow.compat.v1.keras.backend import get_session from human_aware_rl.data_dir import DATA_DIR from human_aware_rl.human.process_dataframes import get_human_human_trajectories @@ -176,6 +175,9 @@ def build_bc_model(use_lstm=True, eager=False, **kwargs): def train_bc_model(model_dir, bc_params, verbose=False): inputs, seq_lens, targets = load_data(bc_params, verbose) + # Ensure targets are int32 for SparseCategoricalCrossentropy + targets = tf.cast(targets, tf.int32) + training_params = bc_params["training_params"] if training_params["use_class_weights"]: @@ -220,7 +222,7 @@ def train_bc_model(model_dir, bc_params, verbose=False): ), # Save checkpoints of the models at the end of every epoch (saving only the best one so far) keras.callbacks.ModelCheckpoint( - filepath=os.path.join(model_dir, "checkpoints"), + filepath=os.path.join(model_dir, "checkpoints", "model.keras"), monitor="loss", save_best_only=True, ), @@ -235,15 +237,25 @@ def train_bc_model(model_dir, bc_params, verbose=False): # Inputs unique to lstm model if bc_params["use_lstm"]: - inputs["seq_in"] = seq_lens - inputs["hidden_in"] = np.zeros((N, bc_params["cell_size"])) - inputs["memory_in"] = np.zeros((N, bc_params["cell_size"])) + inputs["seq_in"] = tf.cast(seq_lens, tf.int32) + inputs["hidden_in"] = tf.zeros((N, bc_params["cell_size"]), dtype=tf.float32) + inputs["memory_in"] = tf.zeros((N, bc_params["cell_size"]), dtype=tf.float32) # Batch size doesn't include time dimension (seq_len) so it should be smaller for rnn model batch_size = 1 if bc_params["use_lstm"] else training_params["batch_size"] + model_inputs = ( + inputs + if not bc_params["use_lstm"] + else { + "Overcooked_observation": inputs, + "seq_in": seq_lens, + "hidden_in": np.zeros((N, bc_params["cell_size"])), + "memory_in": np.zeros((N, bc_params["cell_size"])), + } + ) model.fit( - inputs, - targets, + model_inputs, + targets["logits"], callbacks=callbacks, batch_size=batch_size, epochs=training_params["epochs"], @@ -260,18 +272,20 @@ def train_bc_model(model_dir, bc_params, verbose=False): def save_bc_model(model_dir, model, bc_params, verbose=False): """ - Saves the specified model under the directory model_dir. This creates three items - - assets/ stores information essential to reconstructing the context and tf graph - variables/ stores the model's trainable weights - saved_model.pd the saved state of the model object + Saves the specified model under the directory model_dir. This creates a .keras file + containing the model's architecture, weights, and optimizer state. Additionally, saves a pickled dictionary containing all the parameters used to construct this model at model_dir/metadata.pickle """ if verbose: print("Saving bc model at ", model_dir) - model.save(model_dir, save_format="tf") + + # Save model with .keras extension + model_path = os.path.join(model_dir, "model.keras") + model.save(model_path) + + # Save metadata with open(os.path.join(model_dir, "metadata.pickle"), "wb") as f: pickle.dump(bc_params, f) @@ -283,7 +297,12 @@ def load_bc_model(model_dir, verbose=False): """ if verbose: print("Loading bc model from ", model_dir) - model = keras.models.load_model(model_dir, custom_objects={"tf": tf}) + + # Load model from .keras file + model_path = os.path.join(model_dir, "model.keras") + model = keras.models.load_model(model_path, custom_objects={"tf": tf}) + + # Load metadata with open(os.path.join(model_dir, "metadata.pickle"), "rb") as f: bc_params = pickle.load(f) return model, bc_params @@ -406,40 +425,6 @@ def _build_lstm_model( ################ -class NullContextManager: - """ - No-op context manager that does nothing - """ - - def __init__(self): - pass - - def __enter__(self): - pass - - def __exit__(self, *args): - pass - - -class TfContextManager: - """ - Properly sets the execution graph and session of the keras backend given a "session" object as input - - Used for isolating tf execution in graph mode. Do not use with eager models or with eager mode on - """ - - def __init__(self, session): - self.session = session - - def __enter__(self): - self.ctx = self.session.graph.as_default() - self.ctx.__enter__() - set_session(self.session) - - def __exit__(self, *args): - self.ctx.__exit__(*args) - - class BehaviorCloningPolicy(RllibPolicy): def __init__(self, observation_space, action_space, config): """ @@ -470,8 +455,6 @@ def __init__(self, observation_space, action_space, config): ) model, bc_params = load_bc_model(config["model_dir"]) - # Save the session that the model was loaded into so it is available at inference time if necessary - self._sess = get_session() self._setup_shapes() # Basic check to make sure model dimensions match @@ -482,8 +465,6 @@ def __init__(self, observation_space, action_space, config): self.stochastic = config["stochastic"] self.use_lstm = bc_params["use_lstm"] self.cell_size = bc_params["cell_size"] - self.eager = config["eager"] if "eager" in config else bc_params["eager"] - self.context = self._create_execution_context() def _setup_shapes(self): # This is here to make the class compatible with both tuples or gymnasium.Space objs for the spaces @@ -542,11 +523,8 @@ def compute_actions( # Cast to np.array if list (no-op if already np.array) obs_batch = np.array(obs_batch) - # Run the model - with self.context: - action_logits, states = self._forward(obs_batch, state_batches) + action_logits, states = self._forward(obs_batch, state_batches) - # Softmax in numpy to convert logits to probabilities action_probs = softmax(action_logits) if self.stochastic: # Sample according to action_probs for each row in the output @@ -611,16 +589,6 @@ def _forward(self, obs_batch, state_batches): else: return self.model.predict(obs_batch, verbose=0), [] - def _create_execution_context(self): - """ - Creates a private execution context for the model - - Necessary if using with rllib in order to isolate this policy model from others - """ - if self.eager: - return NullContextManager() - return TfContextManager(self._sess) - if __name__ == "__main__": params = get_bc_params() diff --git a/src/human_aware_rl/imitation/behavior_cloning_tf2_test.py b/src/human_aware_rl/imitation/behavior_cloning_tf2_test.py index 08ae613c..37f04493 100644 --- a/src/human_aware_rl/imitation/behavior_cloning_tf2_test.py +++ b/src/human_aware_rl/imitation/behavior_cloning_tf2_test.py @@ -1,8 +1,10 @@ import argparse +import gc import os import pickle import shutil import sys +import time import unittest import warnings @@ -32,7 +34,6 @@ def _clear_pickle(): class TestBCTraining(unittest.TestCase): - """ Unittests for behavior cloning training and utilities @@ -48,9 +49,9 @@ def __init__(self, test_name): self.compute_pickle = False self.strict = False self.min_performance = 0 - assert not ( - self.compute_pickle and self.strict - ), "Cannot compute pickle and run strict reproducibility tests at same time" + assert not (self.compute_pickle and self.strict), ( + "Cannot compute pickle and run strict reproducibility tests at same time" + ) if self.compute_pickle: _clear_pickle() @@ -94,7 +95,17 @@ def tearDown(self): with open(BC_EXPECTED_DATA_PATH, "wb") as f: pickle.dump(self.expected, f) - shutil.rmtree(self.model_dir) + # Force garbage collection to close any open files + gc.collect() + + # Add a small delay to ensure files are released + time.sleep(0.1) + + try: + # Use ignore_errors=True to force removal even if some files are still locked + shutil.rmtree(self.model_dir, ignore_errors=True) + except Exception as e: + print(f"Warning: Could not fully remove directory {self.model_dir}: {e}") def test_model_construction(self): model = build_bc_model(**self.bc_params) @@ -115,9 +126,7 @@ def test_save_and_load(self): loaded_model, loaded_params = load_bc_model(self.model_dir) self.assertDictEqual(self.bc_params, loaded_params) self.assertTrue( - np.allclose( - model(self.dummy_input), loaded_model(self.dummy_input) - ) + np.allclose(model(self.dummy_input), loaded_model(self.dummy_input)) ) def test_training(self): @@ -127,9 +136,7 @@ def test_training(self): self.expected["test_training"] = model(self.dummy_input) if self.strict: self.assertTrue( - np.allclose( - model(self.dummy_input), self.expected["test_training"] - ) + np.allclose(model(self.dummy_input), self.expected["test_training"]) ) def test_agent_evaluation(self): @@ -143,9 +150,7 @@ def test_agent_evaluation(self): if self.compute_pickle: self.expected["test_agent_evaluation"] = results if self.strict: - self.assertAlmostEqual( - results, self.expected["test_agent_evaluation"] - ) + self.assertAlmostEqual(results, self.expected["test_agent_evaluation"]) class TestBCTrainingLSTM(TestBCTraining): @@ -190,9 +195,7 @@ def test_lstm_evaluation(self): if self.compute_pickle: self.expected["test_lstm_evaluation"] = results if self.strict: - self.assertAlmostEqual( - results, self.expected["test_lstm_evaluation"] - ) + self.assertAlmostEqual(results, self.expected["test_lstm_evaluation"]) def test_lstm_save_and_load(self): self.bc_params["use_lstm"] = True From d86f6301dc0c01c31f0edc7a35bae4f768d4883a Mon Sep 17 00:00:00 2001 From: micah Date: Sat, 22 Mar 2025 11:53:37 -0700 Subject: [PATCH 09/13] Dropped support for training BC / RL policies --- .github/workflows/pythontests.yml | 4 +- Overcooked Tutorial.ipynb | 787 +++++-------- pyproject.toml | 30 +- src/human_aware_rl/imitation/reproduce_bc.py | 10 +- uv.lock | 1037 +++++------------- 5 files changed, 571 insertions(+), 1297 deletions(-) diff --git a/.github/workflows/pythontests.yml b/.github/workflows/pythontests.yml index b6108b34..b4571727 100644 --- a/.github/workflows/pythontests.yml +++ b/.github/workflows/pythontests.yml @@ -17,7 +17,7 @@ jobs: run: | uv venv source .venv/bin/activate - uv pip install -e .[harl] + uv pip install -e . - name: Run tests run: | source .venv/bin/activate @@ -42,7 +42,7 @@ jobs: - name: Setup # needs -e flag for coverage tests to work run: | source .venv/bin/activate - uv pip install -e ".[harl]" + uv pip install -e . - name: Run tests and generate coverage report run: | source .venv/bin/activate diff --git a/Overcooked Tutorial.ipynb b/Overcooked Tutorial.ipynb index 9ce94dbd..ee485688 100644 --- a/Overcooked Tutorial.ipynb +++ b/Overcooked Tutorial.ipynb @@ -9,104 +9,69 @@ "This Notebook will demonstrate a couple of common use cases of the Overcooked-AI library, including loading and evaluating agents and visualizing trajectories.\n" ] }, - { - "cell_type": "markdown", - "id": "aca6b8ba", - "metadata": {}, - "source": [ - "# Getting started: Training your agent\n", - "The most convenient way to train an agent is with the [ppo_rllib_client.py](https://github.com/HumanCompatibleAI/overcooked_ai/blob/master/src/human_aware_rl/ppo/ppo_rllib_client.py) file, where you can either pass in the arguments through commandline, or you can directly modify the variables you want to change in the file. \n", - "\n", - "You can also start an experiment in another python script as done below, which can sometimes be more convenient:" - ] - }, { "cell_type": "code", - "execution_count": 1, - "id": "a60521ac", + "execution_count": 5, + "id": "ca4bad07", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2025-03-22 10:00:22.486568: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2025-03-22 10:00:22.593989: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", - "2025-03-22 10:00:22.594035: I tensorflow/compiler/xla/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n", - "2025-03-22 10:00:23.328094: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", - "2025-03-22 10:00:23.328172: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", - "2025-03-22 10:00:23.328180: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n", - "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/_private/ray_option_utils.py:266: DeprecationWarning: Setting 'object_store_memory' for actors is deprecated since it doesn't actually reserve the required object store memory. Use object spilling that's enabled by default (https://docs.ray.io/en/releases-2.0.0/ray-core/objects/object-spilling.html) instead to bypass the object store memory size limitation.\n", - " warnings.warn(\n", - "2025-03-22 10:00:35.954003: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", - "2025-03-22 10:00:35.954092: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublas.so.11'; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", - "2025-03-22 10:00:35.954135: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcublasLt.so.11'; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", - "2025-03-22 10:00:35.954171: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcufft.so.10'; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", - "2025-03-22 10:00:35.979136: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcusparse.so.11'; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", - "2025-03-22 10:00:35.979225: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/cv2/../../lib64:\n", - "2025-03-22 10:00:35.982989: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2025-03-22 10:00:37.085100: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:357] MLIR V1 optimization pass is not enabled\n", - "2025-03-22 10:00:37.555990: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", - "2025-03-22 10:00:37.658986: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", - "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/rllib/utils/pre_checks/env.py:422: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", - "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", - " if not isinstance(done_, (bool, np.bool, np.bool_)):\n", - "2025-03-22 10:00:38.324521: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", - "2025-03-22 10:00:38.421146: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n" + "Avg rew: 0.00 (std: 0.00, se: 0.00); avg len: 400.00; : 100%|██████████| 10/10 [00:00<00:00, 19.41it/s]\n", + "Avg rew: 180.00 (std: 0.00, se: 0.00); avg len: 400.00; : 100%|██████████| 1/1 [00:00<00:00, 8.01it/s]\n" ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "02c455cfd0a34a6f8eaab9e8627f7697", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(IntSlider(value=0, description='timestep', max=399), Output()), _dom_classes=('widget-in…" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "from human_aware_rl.ppo.ppo_rllib_client import ex\n", - "# For all the tunable paramters, check out ppo_rllib_client.py file\n", - "# Note this is not what the configuration should look like for a real experiment\n", - "config_updates = {\n", - " \"results_dir\": \"tutorial_notebook_results/SP\", # can change this whatever directory you want\n", - " \"layout_name\": \"cramped_room\",\n", - " \"clip_param\": 0.2,\n", - " 'gamma': 0.9,\n", - " 'num_training_iters': 10, #this should usually be a lot higher\n", - " 'num_workers': 1,\n", - " 'num_gpus': 0,\n", - " \"verbose\": False,\n", - " 'train_batch_size': 800,\n", - " 'sgd_minibatch_size': 800,\n", - " 'num_sgd_iter': 1,\n", - " \"evaluation_interval\": 2\n", - "}\n", - "run = ex.run(config_updates=config_updates, options={\"--loglevel\": \"ERROR\"})" + "from overcooked_ai_py.agents.agent import AgentPair, RandomAgent\n", + "from overcooked_ai_py.agents.benchmarking import AgentEvaluator\n", + "from overcooked_ai_py.visualization.state_visualizer import StateVisualizer\n", + "\n", + "# Here we create an evaluator for the cramped_room layout\n", + "layout = \"cramped_room\"\n", + "ae = AgentEvaluator.from_layout_name(mdp_params={\"layout_name\": layout, \"old_dynamics\": True}, \n", + " env_params={\"horizon\": 400})\n", + "\n", + "ap = AgentPair(RandomAgent(), RandomAgent())\n", + "\n", + "trajs = ae.evaluate_agent_pair(ap, 10)\n", + "\n", + "trajs2 = ae.evaluate_human_model_pair(1)\n", + "\n", + "\n", + "StateVisualizer().display_rendered_trajectory(trajs2, ipython_display=True)" ] }, { "cell_type": "markdown", - "id": "96de10c0", + "id": "927f70d1", "metadata": {}, "source": [ - "One can check the results of the experiment run by accessing **run.result**" + "# Deprecated stuff which requires BC and RL training (see README for details)" ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "d356cb34", + "cell_type": "markdown", + "id": "aca6b8ba", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'average_sparse_reward': 0.0, 'average_total_reward': 21.43114766237247}" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "result = run.result\n", - "result" + "# Getting started: Training your agent" ] }, { @@ -114,66 +79,19 @@ "id": "0a8f96f8", "metadata": {}, "source": [ - "In practice, the reward should be much higher if optimized. Checkout the graph in the [README](https://github.com/HumanCompatibleAI/overcooked_ai/tree/master/src/human_aware_rl) in human_aware_rl module for baseline performances.\n", - "\n", - "Similarly, you can train BC agents with the [reproduce_bc.py](https://github.com/HumanCompatibleAI/overcooked_ai/blob/master/src/human_aware_rl/imitation/reproduce_bc.py) file under the human_aware_rl/imitation directory. " + "You can train BC agents using files under the `human_aware_rl/imitation` directory. " ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "7f493c88", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading data from /nas/ucb/micah/overcooked_ai/src/human_aware_rl/static/human_data/cleaned/2019_hh_trials_train.pickle\n", - "Number of trajectories processed for each layout: {'cramped_room': 14}\n", - "Epoch 1/10\n", - "446/446 - 2s - loss: 0.9640 - sparse_categorical_accuracy: 0.7175 - val_loss: 0.8835 - val_sparse_categorical_accuracy: 0.7058 - lr: 0.0010 - 2s/epoch - 4ms/step\n", - "Epoch 2/10\n", - "446/446 - 1s - loss: 0.8487 - sparse_categorical_accuracy: 0.7241 - val_loss: 0.8261 - val_sparse_categorical_accuracy: 0.7042 - lr: 0.0010 - 943ms/epoch - 2ms/step\n", - "Epoch 3/10\n", - "446/446 - 1s - loss: 0.8091 - sparse_categorical_accuracy: 0.7242 - val_loss: 0.7902 - val_sparse_categorical_accuracy: 0.7062 - lr: 0.0010 - 955ms/epoch - 2ms/step\n", - "Epoch 4/10\n", - "446/446 - 1s - loss: 0.7919 - sparse_categorical_accuracy: 0.7252 - val_loss: 0.7774 - val_sparse_categorical_accuracy: 0.7046 - lr: 0.0010 - 954ms/epoch - 2ms/step\n", - "Epoch 5/10\n", - "446/446 - 1s - loss: 0.7780 - sparse_categorical_accuracy: 0.7231 - val_loss: 0.7726 - val_sparse_categorical_accuracy: 0.7040 - lr: 0.0010 - 1s/epoch - 3ms/step\n", - "Epoch 6/10\n", - "446/446 - 1s - loss: 0.7701 - sparse_categorical_accuracy: 0.7224 - val_loss: 0.7773 - val_sparse_categorical_accuracy: 0.7030 - lr: 0.0010 - 970ms/epoch - 2ms/step\n", - "Epoch 7/10\n", - "446/446 - 1s - loss: 0.7630 - sparse_categorical_accuracy: 0.7239 - val_loss: 0.7736 - val_sparse_categorical_accuracy: 0.7008 - lr: 0.0010 - 1s/epoch - 2ms/step\n", - "Epoch 8/10\n", - "446/446 - 1s - loss: 0.7579 - sparse_categorical_accuracy: 0.7223 - val_loss: 0.7653 - val_sparse_categorical_accuracy: 0.7018 - lr: 0.0010 - 884ms/epoch - 2ms/step\n", - "Epoch 9/10\n", - "446/446 - 1s - loss: 0.7540 - sparse_categorical_accuracy: 0.7223 - val_loss: 0.7607 - val_sparse_categorical_accuracy: 0.7042 - lr: 0.0010 - 945ms/epoch - 2ms/step\n", - "Epoch 10/10\n", - "446/446 - 1s - loss: 0.7498 - sparse_categorical_accuracy: 0.7240 - val_loss: 0.7662 - val_sparse_categorical_accuracy: 0.6996 - lr: 0.0010 - 942ms/epoch - 2ms/step\n", - "Saving bc model at tutorial_notebook_results/BC\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "layout = \"cramped_room\" # any compatible layouts \n", - "from human_aware_rl.imitation.behavior_cloning_tf2 import (\n", - " get_bc_params, # get the configuration for BC agents\n", - " train_bc_model, # train the BC model\n", - ")\n", - "from human_aware_rl.static import (\n", - " CLEAN_2019_HUMAN_DATA_TRAIN, # human trajectories\n", - ")\n", + "from human_aware_rl.imitation.behavior_cloning_tf2 import get_bc_params, train_bc_model\n", + "from human_aware_rl.static import CLEAN_2019_HUMAN_DATA_TRAIN\n", "\n", "params_to_override = {\n", " # this is the layouts where the training will happen\n", @@ -190,81 +108,13 @@ "train_bc_model(\"tutorial_notebook_results/BC\", bc_params, verbose = True)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "1acd72b1", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "dc068ebc", "metadata": {}, "source": [ "# 1): Loading trained agents\n", - "This section will show you how to load a pretrained agents. To load an agent, you can use the load_agent function in the [rllib.py](https://github.com/HumanCompatibleAI/overcooked_ai/blob/master/src/human_aware_rl/rllib/rllib.py) file. For the purpose of demonstration, I will be loading a local agent, which is also one of the agents included in the web demo. " - ] - }, - { - "cell_type": "markdown", - "id": "844332c3", - "metadata": {}, - "source": [ - "## 1.1): Loading PPO agent\n", - "The PPO agents are all trained via the Ray trainer, so to load a trained agent, we can just use the load_agent function" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8aeaae41", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/_private/ray_option_utils.py:266: DeprecationWarning: Setting 'object_store_memory' for actors is deprecated since it doesn't actually reserve the required object store memory. Use object spilling that's enabled by default (https://docs.ray.io/en/releases-2.0.0/ray-core/objects/object-spilling.html) instead to bypass the object store memory size limitation.\n", - " warnings.warn(\n", - "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/rllib/utils/pre_checks/env.py:422: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", - "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", - " if not isinstance(done_, (bool, np.bool, np.bool_)):\n", - "2025-03-22 10:01:34.937075: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", - "2025-03-22 10:01:35.053493: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", - "2025-03-22 10:01:35.765484: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", - "2025-03-22 10:01:35.869892: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from human_aware_rl.rllib.rllib import load_agent\n", - "agent_path = \"tutorial_notebook_results/SP/PPO_cramped_room_True_nw=1_vf=0.000100_es=0.200000_en=0.100000_kl=0.200000_0_2025-03-22_09-51-24znllxx0d/\"\n", - "# The first argument is the path to the saved trainer\n", - "# The second argument is the type of agent to load, which only matters if it is not a self-play agent \n", - "# The third argument is the agent_index, which is not directly related to the training\n", - "## It is used in creating the RllibAgent class that is used for evaluation\n", - "ppo_agent = load_agent(agent_path, \"ppo\", 0)\n", - "ppo_agent" - ] - }, - { - "cell_type": "markdown", - "id": "143edeb6", - "metadata": {}, - "source": [ - "This function loads an agent from the trainer. The RllibAgent class is a wrapper around the core policy, which simplifies pairing and evaluating different type of agents.\n" + "This section will show you how to load a pretrained agents. " ] }, { @@ -272,20 +122,20 @@ "id": "1b2a9df6", "metadata": {}, "source": [ - "## 1.2) Loading BC agent\n", + "## 1.1) Loading BC agent\n", "The BC (behavior cloning) agents are trained separately without using Ray. We showed how to train a BC agent in the previous section, and to load a trained agent, we can use the load_bc_model function" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "f94ab2a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(,\n", + "(,\n", " {'eager': True,\n", " 'use_lstm': False,\n", " 'cell_size': 256,\n", @@ -313,7 +163,7 @@ " 'observation_shape': (96,)})" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -336,31 +186,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "68c37a25", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING - tensorflow - OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -376,8 +212,11 @@ "base_env = base_ae.env\n", "\n", "from human_aware_rl.rllib.rllib import RlLibAgent\n", - "bc_agent = RlLibAgent(bc_policy, 0, base_env.featurize_state_mdp)\n", - "bc_agent" + "bc_agent0 = RlLibAgent(bc_policy, 0, base_env.featurize_state_mdp)\n", + "bc_agent0\n", + "\n", + "bc_agent1 = RlLibAgent(bc_policy, 1, base_env.featurize_state_mdp)\n", + "bc_agent1" ] }, { @@ -398,79 +237,6 @@ "To do evaluation, we need a pair of agents, or an AgentPair. We can directly load a pair of agents for evaluation, which we can do with the load_agent_pair function, or we can create an AgentPair manually from 2 separate RllibAgent instance. To directly load an AgentPair from a trainer:" ] }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b2c3f7d6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/_private/ray_option_utils.py:266: DeprecationWarning: Setting 'object_store_memory' for actors is deprecated since it doesn't actually reserve the required object store memory. Use object spilling that's enabled by default (https://docs.ray.io/en/releases-2.0.0/ray-core/objects/object-spilling.html) instead to bypass the object store memory size limitation.\n", - " warnings.warn(\n", - "/nas/ucb/micah/overcooked_ai/.venv/lib/python3.10/site-packages/ray/rllib/utils/pre_checks/env.py:422: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", - "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", - " if not isinstance(done_, (bool, np.bool, np.bool_)):\n", - "2025-03-22 10:01:46.980300: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", - "2025-03-22 10:01:47.086265: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", - "2025-03-22 10:01:47.780459: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/lr/Assign' id:321 op device:{requested: '', assigned: ''} def:{{{node ppo/lr/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/lr, ppo/lr/Initializer/initial_value)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n", - "2025-03-22 10:01:47.885295: W tensorflow/c/c_api.cc:291] Operation '{name:'ppo/ppo/dense_4/bias/Adam_1/Assign' id:1270 op device:{requested: '', assigned: ''} def:{{{node ppo/ppo/dense_4/bias/Adam_1/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](ppo/ppo/dense_4/bias/Adam_1, ppo/ppo/dense_4/bias/Adam_1/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from human_aware_rl.rllib.rllib import load_agent_pair\n", - "# if we want to load a self-play agent\n", - "ap_sp = load_agent_pair(agent_path,\"ppo\",\"ppo\")\n", - "ap_sp " - ] - }, - { - "cell_type": "markdown", - "id": "249778ce", - "metadata": {}, - "source": [ - "This is convenient when the agents trained are not self-play agents. For example, if we have a PPO agent trained with a BC agent, we can load both as an agent pair at the same time. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c78bb724", - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'tutorial_notebook_results/config.pkl'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m bc_agent_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtutorial_notebook_results/BC\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m ap_bc \u001b[38;5;241m=\u001b[39m \u001b[43mload_agent_pair\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbc_agent_path\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mppo\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m ap_bc\n", - "File \u001b[0;32m/nas/ucb/micah/overcooked_ai/src/human_aware_rl/rllib/rllib.py:876\u001b[0m, in \u001b[0;36mload_agent_pair\u001b[0;34m(save_path, policy_id_0, policy_id_1)\u001b[0m\n\u001b[1;32m 871\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mload_agent_pair\u001b[39m(save_path, policy_id_0\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mppo\u001b[39m\u001b[38;5;124m\"\u001b[39m, policy_id_1\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mppo\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 872\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 873\u001b[0m \u001b[38;5;124;03m Returns an Overcooked AgentPair object that has as player 0 and player 1 policies with\u001b[39;00m\n\u001b[1;32m 874\u001b[0m \u001b[38;5;124;03m ID policy_id_0 and policy_id_1, respectively\u001b[39;00m\n\u001b[1;32m 875\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 876\u001b[0m trainer \u001b[38;5;241m=\u001b[39m \u001b[43mload_trainer\u001b[49m\u001b[43m(\u001b[49m\u001b[43msave_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 877\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_agent_pair_from_trainer(trainer, policy_id_0, policy_id_1)\n", - "File \u001b[0;32m/nas/ucb/micah/overcooked_ai/src/human_aware_rl/rllib/rllib.py:816\u001b[0m, in \u001b[0;36mload_trainer\u001b[0;34m(save_path, true_num_workers)\u001b[0m\n\u001b[1;32m 814\u001b[0m \u001b[38;5;66;03m# Read in params used to create trainer\u001b[39;00m\n\u001b[1;32m 815\u001b[0m config_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mdirname(save_path), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 816\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mconfig_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 817\u001b[0m \u001b[38;5;66;03m# We use dill (instead of pickle) here because we must deserialize functions\u001b[39;00m\n\u001b[1;32m 818\u001b[0m config \u001b[38;5;241m=\u001b[39m dill\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m 819\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m true_num_workers:\n\u001b[1;32m 820\u001b[0m \u001b[38;5;66;03m# Override this param to lower overhead in trainer creation\u001b[39;00m\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'tutorial_notebook_results/config.pkl'" - ] - } - ], - "source": [ - "bc_agent_path = \"tutorial_notebook_results/BC\"\n", - "ap_bc = load_agent_pair(bc_agent_path,\"ppo\",\"bc\")\n", - "ap_bc" - ] - }, { "cell_type": "markdown", "id": "c8bd83bc", @@ -481,25 +247,25 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "f0acdeee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from human_aware_rl.rllib.rllib import AgentPair\n", - "ap = AgentPair(ppo_agent,bc_agent)\n", - "ap" + "ap_bc = AgentPair(bc_agent0, bc_agent1)\n", + "ap_bc" ] }, { @@ -518,17 +284,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "id": "95787dc6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -552,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "93676beb", "metadata": {}, "outputs": [ @@ -560,13 +326,152 @@ "name": "stderr", "output_type": "stream", "text": [ - "Avg rew: 0.00 (std: 0.00, se: 0.00); avg len: 400.00; : 100%|██████████| 10/10 [00:09<00:00, 1.01it/s]\n" + "Avg rew: 58.00 (std: 24.41, se: 7.72); avg len: 400.00; : 100%|██████████| 10/10 [06:57<00:00, 41.80s/it]\n" ] }, { "data": { "text/plain": [ - "{'env_params': array([{'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", + "{'ep_actions': array([[((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 1), (0, 0)), ...,\n", + " ((1, 0), (0, 0)), ((0, -1), (0, 0)), ((0, 0), (0, 0))],\n", + " [((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0)), ...,\n", + " ((0, -1), (0, 0)), ('interact', (0, 0)), ((1, 0), (0, 0))],\n", + " [((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, -1), (0, 0)), ...,\n", + " ((-1, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0))],\n", + " ...,\n", + " [((-1, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0)), ...,\n", + " ((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0))],\n", + " [((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0)), ...,\n", + " ((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0))],\n", + " [((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0)), ...,\n", + " ((0, 0), (0, 0)), ((0, 0), (0, 0)), ((0, 0), (0, 0))]],\n", + " dtype=object),\n", + " 'metadatas': {},\n", + " 'ep_infos': array([[{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 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[]], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([20, 60]), 'cumulative_shaped_rewards_by_agent': array([44, 36])}, 'ep_sparse_r': 80, 'ep_shaped_r': 80, 'ep_sparse_r_by_agent': array([20, 60]), 'ep_shaped_r_by_agent': array([44, 36]), 'ep_length': 400}}],\n", + " [{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[0.43064207, 0.00118634, 0.04243125, 0.01245082, 0.46324122,\n", + " 0.05004829]], dtype=float32)}, {'action_probs': array([[0.07216967, 0.01146591, 0.09417965, 0.00753717, 0.7981282 ,\n", + " 0.01651939]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.035366 , 0.00080447, 0.00464188, 0.00211906, 0.30970713,\n", + " 0.64736146]], dtype=float32)}, {'action_probs': array([[0.08782221, 0.01013853, 0.09689118, 0.00953836, 0.7771887 ,\n", + " 0.018421 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [3, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.00814912, 0.00546082, 0.30809313, 0.02386194, 0.62155545,\n", + " 0.03287959]], dtype=float32)}, {'action_probs': array([[0.07929008, 0.02225238, 0.06561285, 0.00576123, 0.8015312 ,\n", + " 0.0255523 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[10, 30, 38, 73, 107, 128, 176, 388], []], 'useful_onion_pickup': [[10, 30, 38, 73, 107, 128, 176, 388], []], 'onion_drop': [[], []], 'useful_onion_drop': [[], []], 'potting_onion': [[22, 36, 48, 100, 111, 166, 351, 398], []], 'dish_pickup': [[], [63, 96, 395]], 'useful_dish_pickup': [[], [63, 395]], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[], [78, 323]], 'soup_delivery': [[], [87, 347]], 'soup_drop': [[], []], 'optimal_onion_potting': [[22, 36, 48, 100, 111, 166, 351, 398], []], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[22, 36, 48, 100, 111, 166, 351, 398], []], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([ 0, 40]), 'cumulative_shaped_rewards_by_agent': array([24, 16])}, 'ep_sparse_r': 40, 'ep_shaped_r': 40, 'ep_sparse_r_by_agent': array([ 0, 40]), 'ep_shaped_r_by_agent': array([24, 16]), 'ep_length': 400}}],\n", + " [{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[1.9425798e-02, 2.5771598e-03, 3.3497889e-04, 1.6918801e-01,\n", + " 8.0651075e-01, 1.9632669e-03]], dtype=float32)}, {'action_probs': array([[9.9211924e-02, 1.4156876e-02, 1.3569271e-02, 1.5255912e-03,\n", + " 8.7080568e-01, 7.3057896e-04]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[1.9425798e-02, 2.5771598e-03, 3.3497889e-04, 1.6918801e-01,\n", + " 8.0651075e-01, 1.9632669e-03]], dtype=float32)}, {'action_probs': array([[9.9211924e-02, 1.4156876e-02, 1.3569271e-02, 1.5255912e-03,\n", + " 8.7080568e-01, 7.3057896e-04]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[1.9425798e-02, 2.5771598e-03, 3.3497889e-04, 1.6918801e-01,\n", + " 8.0651075e-01, 1.9632669e-03]], dtype=float32)}, {'action_probs': array([[9.9211924e-02, 1.4156876e-02, 1.3569271e-02, 1.5255912e-03,\n", + " 8.7080568e-01, 7.3057896e-04]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[5, 12, 42, 67, 81, 101, 132, 143, 214, 228, 257, 299, 339, 365, 379], [7, 151, 299]], 'useful_onion_pickup': [[5, 12, 42, 67, 81, 101, 132, 143, 214, 228, 257, 299, 365, 379], [7, 151, 299]], 'onion_drop': [[], []], 'useful_onion_drop': [[], []], 'potting_onion': [[9, 28, 63, 77, 94, 124, 137, 154, 222, 238, 294, 322, 359, 372], [20, 196, 307]], 'dish_pickup': [[161], [26, 93, 224, 342, 368]], 'useful_dish_pickup': [[161], [26, 93, 224, 342, 368]], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[185], [49, 115, 264, 350]], 'soup_delivery': [[], [62, 126, 280, 358]], 'soup_drop': [[190], []], 'optimal_onion_potting': [[9, 28, 63, 77, 94, 124, 137, 154, 222, 238, 294, 322, 359, 372], [20, 196, 307]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[9, 28, 63, 77, 94, 124, 137, 154, 222, 238, 294, 322, 359, 372], [20, 196, 307]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([ 0, 80]), 'cumulative_shaped_rewards_by_agent': array([50, 44])}, 'ep_sparse_r': 80, 'ep_shaped_r': 94, 'ep_sparse_r_by_agent': array([ 0, 80]), 'ep_shaped_r_by_agent': array([50, 44]), 'ep_length': 400}}],\n", + " ...,\n", + " [{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.15208364, 0.02959962, 0.03100314, 0.09762987, 0.6794261 ,\n", + " 0.01025762]], dtype=float32)}, {'action_probs': array([[0.01076664, 0.00366979, 0.01067814, 0.01863392, 0.94092256,\n", + " 0.01532892]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.15208364, 0.02959962, 0.03100314, 0.09762987, 0.6794261 ,\n", + " 0.01025762]], dtype=float32)}, {'action_probs': array([[0.01076664, 0.00366979, 0.01067814, 0.01863392, 0.94092256,\n", + " 0.01532892]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[0.02323768, 0.01111274, 0.00306184, 0.0065601 , 0.95041126,\n", + " 0.00561639]], dtype=float32)}, {'action_probs': array([[0.01019176, 0.00143141, 0.0016537 , 0.24706495, 0.72344553,\n", + " 0.01621261]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.02323768, 0.01111274, 0.00306184, 0.0065601 , 0.95041126,\n", + " 0.00561639]], dtype=float32)}, {'action_probs': array([[0.01019176, 0.00143141, 0.0016537 , 0.24706495, 0.72344553,\n", + " 0.01621261]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.02323768, 0.01111274, 0.00306184, 0.0065601 , 0.95041126,\n", + " 0.00561639]], dtype=float32)}, {'action_probs': array([[0.01019176, 0.00143141, 0.0016537 , 0.24706495, 0.72344553,\n", + " 0.01621261]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[], [59, 84, 94, 105, 146, 160, 190, 211, 224, 252, 266, 308, 337, 364, 392]], 'useful_onion_pickup': [[], [59, 84, 94, 105, 146, 160, 190, 211, 224, 252, 266, 308, 337, 364, 392]], 'onion_drop': [[], []], 'useful_onion_drop': [[], []], 'potting_onion': [[], [75, 90, 98, 135, 152, 165, 207, 216, 227, 261, 272, 326, 361, 371]], 'dish_pickup': [[104, 159, 237, 284, 368], []], 'useful_dish_pickup': [[104, 159, 237, 284, 368], []], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[122, 189, 250, 348], [286]], 'soup_delivery': [[201, 257], [296]], 'soup_drop': [[130, 362], []], 'optimal_onion_potting': [[], [75, 90, 98, 135, 152, 165, 207, 216, 227, 261, 272, 326, 361, 371]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[], [75, 90, 98, 135, 152, 165, 207, 216, 227, 261, 272, 326, 361, 371]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([40, 20]), 'cumulative_shaped_rewards_by_agent': array([35, 42])}, 'ep_sparse_r': 60, 'ep_shaped_r': 77, 'ep_sparse_r_by_agent': array([40, 20]), 'ep_shaped_r_by_agent': array([35, 42]), 'ep_length': 400}}],\n", + " [{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[0.01547191, 0.00609912, 0.01313627, 0.04950164, 0.81513834,\n", + " 0.10065278]], dtype=float32)}, {'action_probs': array([[0.10248725, 0.00658033, 0.1326387 , 0.01534621, 0.74053526,\n", + " 0.00241225]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.01547191, 0.00609912, 0.01313627, 0.04950164, 0.81513834,\n", + " 0.10065278]], dtype=float32)}, {'action_probs': array([[0.10248725, 0.00658033, 0.1326387 , 0.01534621, 0.74053526,\n", + " 0.00241225]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.01547191, 0.00609912, 0.01313627, 0.04950164, 0.81513834,\n", + " 0.10065278]], dtype=float32)}, {'action_probs': array([[0.10248725, 0.00658033, 0.1326387 , 0.01534621, 0.74053526,\n", + " 0.00241225]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[34, 53, 71, 99, 140, 177, 210, 226, 269, 280, 292, 303, 337], [11]], 'useful_onion_pickup': [[34, 53, 71, 99, 140, 177, 210, 226, 269, 280, 292, 303, 337], [11]], 'onion_drop': [[], []], 'useful_onion_drop': [[], []], 'potting_onion': [[41, 64, 94, 105, 171, 207, 220, 233, 276, 288, 298, 328, 355], [29]], 'dish_pickup': [[], [42, 104, 210, 300, 343]], 'useful_dish_pickup': [[], [42, 104, 210, 300, 343]], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[], [87, 192, 268, 318]], 'soup_delivery': [[], [90, 196, 289, 329]], 'soup_drop': [[], []], 'optimal_onion_potting': [[41, 64, 94, 105, 171, 207, 220, 233, 276, 288, 298, 328, 355], [29]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[41, 64, 94, 105, 171, 207, 220, 233, 276, 288, 298, 328, 355], [29]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([ 0, 80]), 'cumulative_shaped_rewards_by_agent': array([39, 38])}, 'ep_sparse_r': 80, 'ep_shaped_r': 77, 'ep_sparse_r_by_agent': array([ 0, 80]), 'ep_shaped_r_by_agent': array([39, 38]), 'ep_length': 400}}],\n", + " [{'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.05297125, 0.00369196, 0.00564479, 0.01859784, 0.9077534 ,\n", + " 0.01134084]], dtype=float32)}, {'action_probs': array([[0.0172119 , 0.00448586, 0.01028795, 0.02042776, 0.9353154 ,\n", + " 0.01227117]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ...,\n", + " {'agent_infos': [{'action_probs': array([[0.02207099, 0.10103653, 0.00525927, 0.08316644, 0.66694003,\n", + " 0.12152667]], dtype=float32)}, {'action_probs': array([[0.0107964 , 0.00767973, 0.00561914, 0.05614307, 0.90523726,\n", + " 0.01452435]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.02207099, 0.10103653, 0.00525927, 0.08316644, 0.66694003,\n", + " 0.12152667]], dtype=float32)}, {'action_probs': array([[0.0107964 , 0.00767973, 0.00561914, 0.05614307, 0.90523726,\n", + " 0.01452435]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " {'agent_infos': [{'action_probs': array([[0.02207099, 0.10103653, 0.00525927, 0.08316644, 0.66694003,\n", + " 0.12152667]], dtype=float32)}, {'action_probs': array([[0.0107964 , 0.00767973, 0.00561914, 0.05614307, 0.90523726,\n", + " 0.01452435]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[27, 229, 306, 315, 317, 377, 383], [30, 58, 75, 157, 175, 211]], 'useful_onion_pickup': [[27], [30, 58, 75, 157, 175]], 'onion_drop': [[301, 308, 316, 370, 382, 392], []], 'useful_onion_drop': [[301, 308, 316, 370, 382, 392], []], 'potting_onion': [[33], [49, 73, 136, 161, 183]], 'dish_pickup': [[52], []], 'useful_dish_pickup': [[52], []], 'dish_drop': [[], []], 'useful_dish_drop': [[], []], 'soup_pickup': [[110], []], 'soup_delivery': [[129], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[33], [49, 73, 136, 161, 183]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[33], [49, 73, 136, 161, 183]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([20, 0]), 'cumulative_shaped_rewards_by_agent': array([11, 15])}, 'ep_sparse_r': 20, 'ep_shaped_r': 26, 'ep_sparse_r_by_agent': array([20, 0]), 'ep_shaped_r_by_agent': array([11, 15]), 'ep_length': 400}}]],\n", + " dtype=object),\n", + " 'ep_dones': array([[False, False, False, ..., False, False, True],\n", + " [False, False, False, ..., False, False, True],\n", + " [False, False, False, ..., False, False, True],\n", + " ...,\n", + " [False, False, False, ..., False, False, True],\n", + " [False, False, False, ..., False, False, True],\n", + " [False, False, False, ..., False, False, True]], dtype=object),\n", + " 'ep_returns': array([80, 40, 80, 40, 80, 80, 20, 60, 80, 20]),\n", + " 'env_params': array([{'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", @@ -577,6 +482,7 @@ " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1},\n", " {'start_state_fn': None, 'horizon': 400, 'info_level': 0, 'num_mdp': 1}],\n", " dtype=object),\n", + " 'ep_lengths': array([400, 400, 400, 400, 400, 400, 400, 400, 400, 400]),\n", " 'mdp_params': array([{'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", @@ -588,75 +494,6 @@ " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]},\n", " {'layout_name': 'cramped_room', 'terrain': [['X', 'X', 'P', 'X', 'X'], ['O', ' ', ' ', ' ', 'O'], ['X', ' ', ' ', ' ', 'X'], ['X', 'D', 'X', 'S', 'X']], 'start_player_positions': [(1, 2), (3, 1)], 'start_bonus_orders': [], 'rew_shaping_params': {'PLACEMENT_IN_POT_REW': 3, 'DISH_PICKUP_REWARD': 3, 'SOUP_PICKUP_REWARD': 5, 'DISH_DISP_DISTANCE_REW': 0, 'POT_DISTANCE_REW': 0, 'SOUP_DISTANCE_REW': 0}, 'start_all_orders': [{'ingredients': ['onion', 'onion', 'onion']}]}],\n", " dtype=object),\n", - " 'ep_returns': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n", - " 'metadatas': {},\n", - " 'ep_states': array([[,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " ...,\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ],\n", - " [,\n", - " ,\n", - " ,\n", - " ...,\n", - " ,\n", - " ,\n", - " ]],\n", - " dtype=object),\n", - " 'ep_dones': array([[False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True],\n", - " ...,\n", - " [False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True],\n", - " [False, False, False, ..., False, False, True]], dtype=object),\n", - " 'ep_lengths': array([400, 400, 400, 400, 400, 400, 400, 400, 400, 400]),\n", - " 'ep_actions': array([[((-1, 0), 'interact'), ((-1, 0), (0, 1)), ((0, 0), (-1, 0)), ...,\n", - " ((-1, 0), (0, -1)), ((0, 0), (1, 0)), ((0, 0), (0, 0))],\n", - " [((-1, 0), (0, 1)), ('interact', (0, 1)), ((-1, 0), (0, 0)), ...,\n", - " ((1, 0), (1, 0)), ((0, 1), (1, 0)), ((0, 1), (0, 1))],\n", - " [((-1, 0), (-1, 0)), ('interact', 'interact'), ((-1, 0), (0, -1)),\n", - " ..., ((-1, 0), (0, -1)), ((1, 0), (0, 0)), ((0, 1), (-1, 0))],\n", - " ...,\n", - " [((-1, 0), (1, 0)), ((0, 0), (0, 1)), ((0, 0), (-1, 0)), ...,\n", - " ('interact', (0, 1)), ((1, 0), 'interact'), ('interact', (0, 0))],\n", - " [('interact', (-1, 0)), ((0, 1), 'interact'),\n", - " ('interact', (0, 1)), ..., ((0, 1), (0, -1)),\n", - " ('interact', (0, 0)), ('interact', (1, 0))],\n", - " [((0, 1), (1, 0)), ('interact', 'interact'), ((0, 0), (0, 0)),\n", - " ..., ((0, 1), (1, 0)), ((-1, 0), 'interact'), ((0, 0), (0, 1))]],\n", - " dtype=object),\n", " 'ep_rewards': array([[0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", @@ -664,136 +501,62 @@ " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0],\n", " [0, 0, 0, ..., 0, 0, 0]], dtype=object),\n", - " 'ep_infos': array([[{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", - " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", - " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.1623392 , 0.1630508 , 0.16890894, 0.17439182, 0.16053832,\n", - " 0.17077097]], dtype=float32)}, {'action_probs': array([[0.16287003, 0.15844585, 0.17118943, 0.17690448, 0.15579863,\n", - " 0.17479159]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16197926, 0.16039927, 0.17061438, 0.17756605, 0.15855333,\n", - " 0.17088772]], dtype=float32)}, {'action_probs': array([[0.16313884, 0.1595657 , 0.17233239, 0.17709668, 0.1553399 ,\n", - " 0.17252648]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " 'ep_states': array([[,\n", + " ,\n", + " ,\n", " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.15993053, 0.16085851, 0.17144653, 0.18253618, 0.15260945,\n", - " 0.17261879]], dtype=float32)}, {'action_probs': array([[0.1580302 , 0.15622565, 0.1738706 , 0.1841905 , 0.15395913,\n", - " 0.17372395]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.1598108 , 0.16160344, 0.1716468 , 0.18032824, 0.15486619,\n", - " 0.1717446 ]], dtype=float32)}, {'action_probs': array([[0.1588376 , 0.1582854 , 0.17497034, 0.18399468, 0.1505695 ,\n", - " 0.17334254]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.15886615, 0.15968071, 0.17219451, 0.1822429 , 0.15472022,\n", - " 0.17229551]], dtype=float32)}, {'action_probs': array([[0.1602402 , 0.1593841 , 0.17339385, 0.18539803, 0.15018778,\n", - " 0.17139602]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[46, 69], [185]], 'useful_onion_pickup': [[46, 69], [185]], 'onion_drop': [[67, 195], [186]], 'useful_onion_drop': [[], []], 'potting_onion': [[], []], 'dish_pickup': [[4, 199, 301], [39, 69, 78, 199]], 'useful_dish_pickup': [[], []], 'dish_drop': [[20, 259], [40, 72, 177]], 'useful_dish_drop': [[20, 259], [40]], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[], []], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[], []], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([0, 0])}, 'ep_sparse_r': 0, 'ep_shaped_r': 0, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([0, 0]), 'ep_length': 400}}],\n", - " [{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", - " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", - " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16197926, 0.16039927, 0.17061438, 0.17756605, 0.15855333,\n", - " 0.17088772]], dtype=float32)}, {'action_probs': array([[0.16313884, 0.1595657 , 0.17233239, 0.17709668, 0.1553399 ,\n", - " 0.17252648]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16197926, 0.16039927, 0.17061438, 0.17756605, 0.15855333,\n", - " 0.17088772]], dtype=float32)}, {'action_probs': array([[0.16313884, 0.1595657 , 0.17233239, 0.17709668, 0.1553399 ,\n", - " 0.17252648]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.16150971, 0.15739854, 0.17216079, 0.18092592, 0.15272772,\n", - " 0.17527735]], dtype=float32)}, {'action_probs': array([[0.16083288, 0.15678203, 0.16982326, 0.1880148 , 0.14987774,\n", - " 0.17466922]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16056532, 0.15667738, 0.17201836, 0.1833358 , 0.15215969,\n", - " 0.17524344]], dtype=float32)}, {'action_probs': array([[0.15802443, 0.15742199, 0.16922013, 0.18876569, 0.1512885 ,\n", - " 0.17527924]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.15822528, 0.15748751, 0.17339888, 0.18383262, 0.15412685,\n", - " 0.17292893]], dtype=float32)}, {'action_probs': array([[0.15921178, 0.1576472 , 0.17033754, 0.18801689, 0.14991488,\n", - " 0.17487173]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[16], [94, 126, 145, 277]], 'useful_onion_pickup': [[16], [94, 126, 145, 277]], 'onion_drop': [[28], [95, 137]], 'useful_onion_drop': [[], []], 'potting_onion': [[], [186]], 'dish_pickup': [[62, 121, 128, 201, 238, 247, 253, 289], [105, 211, 213, 238]], 'useful_dish_pickup': [[], []], 'dish_drop': [[80, 122, 200, 229, 239, 248, 286], [113, 212, 220, 270]], 'useful_dish_drop': [[80, 122, 200, 229, 239, 248], [113, 212, 220, 270]], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[], [186]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[], [186]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([0, 3])}, 'ep_sparse_r': 0, 'ep_shaped_r': 3, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([0, 3]), 'ep_length': 400}}],\n", - " [{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", - " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", - " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16227834, 0.16232169, 0.16935293, 0.17413795, 0.16104321,\n", - " 0.17086588]], dtype=float32)}, {'action_probs': array([[0.16189973, 0.15962976, 0.1701305 , 0.17654435, 0.15904188,\n", - " 0.17275378]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16227834, 0.16232169, 0.16935293, 0.17413795, 0.16104321,\n", - " 0.17086588]], dtype=float32)}, {'action_probs': array([[0.16189973, 0.15962976, 0.1701305 , 0.17654435, 0.15904188,\n", - " 0.17275378]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.1535736 , 0.15580149, 0.17203628, 0.18693896, 0.15164696,\n", - " 0.18000278]], dtype=float32)}, {'action_probs': array([[0.15312731, 0.15309116, 0.17424197, 0.18629238, 0.15372464,\n", - " 0.17952257]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.15425156, 0.15339772, 0.17078793, 0.18610641, 0.15579702,\n", - " 0.17965937]], dtype=float32)}, {'action_probs': array([[0.15268444, 0.15038447, 0.1726484 , 0.18872282, 0.15364435,\n", - " 0.1819155 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.15396078, 0.1526514 , 0.17319334, 0.18596134, 0.15419598,\n", - " 0.1800372 ]], dtype=float32)}, {'action_probs': array([[0.1534383 , 0.1527064 , 0.17054729, 0.18942283, 0.15487906,\n", - " 0.17900614]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[53, 99, 123, 138, 158, 196, 279], [4, 26, 73, 269]], 'useful_onion_pickup': [[53, 99, 123, 138, 158, 196], [4, 26, 73]], 'onion_drop': [[95, 106, 131, 151], [19, 31]], 'useful_onion_drop': [[], []], 'potting_onion': [[183, 240], [99]], 'dish_pickup': [[11, 20, 49], [102, 182]], 'useful_dish_pickup': [[], []], 'dish_drop': [[17, 41, 50], [178, 242]], 'useful_dish_drop': [[41, 50], []], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[183, 240], [99]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[183, 240], [99]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([6, 3])}, 'ep_sparse_r': 0, 'ep_shaped_r': 9, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([6, 3]), 'ep_length': 400}}],\n", + " ,\n", + " ,\n", + " ],\n", " ...,\n", - " [{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", - " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", - " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16080128, 0.16186069, 0.17070664, 0.17618288, 0.15858963,\n", - " 0.17185883]], dtype=float32)}, {'action_probs': array([[0.16204323, 0.16261593, 0.17061092, 0.17643037, 0.15447804,\n", - " 0.17382143]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16197926, 0.16039927, 0.17061438, 0.17756605, 0.15855333,\n", - " 0.17088772]], dtype=float32)}, {'action_probs': array([[0.16313884, 0.1595657 , 0.17233239, 0.17709668, 0.1553399 ,\n", - " 0.17252648]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " [,\n", + " ,\n", + " ,\n", " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.16286197, 0.15867832, 0.16835809, 0.17968196, 0.15770264,\n", - " 0.17271702]], dtype=float32)}, {'action_probs': array([[0.16237123, 0.15687174, 0.1692933 , 0.1824306 , 0.15662467,\n", - " 0.17240852]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16298842, 0.16081528, 0.17009439, 0.1773307 , 0.15710099,\n", - " 0.17167023]], dtype=float32)}, {'action_probs': array([[0.16168131, 0.15679151, 0.16888632, 0.18232702, 0.1572555 ,\n", - " 0.17305829]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16025929, 0.15560469, 0.17220578, 0.18345937, 0.15310198,\n", - " 0.17536889]], dtype=float32)}, {'action_probs': array([[0.16014256, 0.15642364, 0.17074463, 0.1828058 , 0.15565784,\n", - " 0.17422554]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[51, 108, 179, 397], [48, 350]], 'useful_onion_pickup': [[51, 108, 179, 397], [48, 350]], 'onion_drop': [[155], [56, 398]], 'useful_onion_drop': [[], []], 'potting_onion': [[103, 385], []], 'dish_pickup': [[13, 32], [96, 131, 196, 218]], 'useful_dish_pickup': [[], []], 'dish_drop': [[29, 33], [124, 192, 202, 339]], 'useful_dish_drop': [[29, 33], []], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[103, 385], []], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[103, 385], []], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([6, 0])}, 'ep_sparse_r': 0, 'ep_shaped_r': 6, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([6, 0]), 'ep_length': 400}}],\n", - " [{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", - " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", - " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16092269, 0.16023342, 0.16941634, 0.17624405, 0.16167772,\n", - " 0.17150569]], dtype=float32)}, {'action_probs': array([[0.16210791, 0.15858705, 0.16918093, 0.17562182, 0.16091609,\n", - " 0.1735861 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.1623293 , 0.1612435 , 0.16958475, 0.17623486, 0.15883368,\n", - " 0.17177396]], dtype=float32)}, {'action_probs': array([[0.16151842, 0.15937427, 0.1686882 , 0.17545947, 0.1609162 ,\n", - " 0.1740434 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.15869907, 0.15680926, 0.16976228, 0.18236782, 0.15642159,\n", - " 0.17594 ]], dtype=float32)}, {'action_probs': array([[0.15731654, 0.15634032, 0.16972028, 0.18378621, 0.15592867,\n", - " 0.17690791]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16074032, 0.15602946, 0.1691479 , 0.18132621, 0.15754294,\n", - " 0.17521319]], dtype=float32)}, {'action_probs': array([[0.15789399, 0.15644369, 0.16837148, 0.18325059, 0.157765 ,\n", - " 0.17627522]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16074032, 0.15602946, 0.1691479 , 0.18132621, 0.15754294,\n", - " 0.17521319]], dtype=float32)}, {'action_probs': array([[0.15789399, 0.15644369, 0.16837148, 0.18325059, 0.157765 ,\n", - " 0.17627522]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[65, 321], [7, 20, 35, 164, 246, 267, 355]], 'useful_onion_pickup': [[65, 321], [7, 20, 35, 164, 246, 267, 355]], 'onion_drop': [[76], [18, 34, 49, 269, 365]], 'useful_onion_drop': [[], []], 'potting_onion': [[], [208, 264]], 'dish_pickup': [[2, 92, 312], [65, 157, 278, 386]], 'useful_dish_pickup': [[], []], 'dish_drop': [[18, 260, 314], [132, 160, 346]], 'useful_dish_drop': [[314], [132, 160]], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[], [208, 264]], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[], [208, 264]], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([0, 6])}, 'ep_sparse_r': 0, 'ep_shaped_r': 6, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([0, 6]), 'ep_length': 400}}],\n", - " [{'agent_infos': [{'action_probs': array([[0.16044214, 0.16076425, 0.17014563, 0.17589368, 0.16023868,\n", - " 0.17251562]], dtype=float32)}, {'action_probs': array([[0.16299166, 0.15722479, 0.16982743, 0.17711645, 0.15736254,\n", - " 0.17547713]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.15948316, 0.15902728, 0.1712314 , 0.1780716 , 0.1583024 ,\n", - " 0.17388414]], dtype=float32)}, {'action_probs': array([[0.16246496, 0.16179901, 0.16925995, 0.17648451, 0.1563012 ,\n", - " 0.17369042]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.15906811, 0.15726395, 0.16982809, 0.1796061 , 0.1569173 ,\n", - " 0.17731644]], dtype=float32)}, {'action_probs': array([[0.16171454, 0.16128732, 0.16824189, 0.17827979, 0.15515633,\n", - " 0.17532018]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", " ...,\n", - " {'agent_infos': [{'action_probs': array([[0.1631158 , 0.15849571, 0.17120782, 0.1826241 , 0.15444121,\n", - " 0.1701154 ]], dtype=float32)}, {'action_probs': array([[0.1626232 , 0.15373537, 0.17325418, 0.18292232, 0.1559268 ,\n", - " 0.17153817]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16206643, 0.15531006, 0.17192438, 0.1850254 , 0.15363607,\n", - " 0.1720377 ]], dtype=float32)}, {'action_probs': array([[0.16290885, 0.1547548 , 0.17357878, 0.18625206, 0.15238535,\n", - " 0.17012016]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None},\n", - " {'agent_infos': [{'action_probs': array([[0.16252157, 0.156733 , 0.17254008, 0.1829578 , 0.15431635,\n", - " 0.17093115]], dtype=float32)}, {'action_probs': array([[0.16419952, 0.15702602, 0.17338566, 0.1850904 , 0.15044029,\n", - " 0.1698581 ]], dtype=float32)}], 'sparse_r_by_agent': [0, 0], 'shaped_r_by_agent': [0, 0], 'phi_s': None, 'phi_s_prime': None, 'episode': {'ep_game_stats': {'tomato_pickup': [[], []], 'useful_tomato_pickup': [[], []], 'tomato_drop': [[], []], 'useful_tomato_drop': [[], []], 'potting_tomato': [[], []], 'onion_pickup': [[113], [1, 97, 128, 156, 183]], 'useful_onion_pickup': [[113], [1, 97, 128, 156, 183]], 'onion_drop': [[], [16, 114, 155, 160, 188]], 'useful_onion_drop': [[], []], 'potting_onion': [[136], []], 'dish_pickup': [[1, 64, 76, 105, 148], [205, 261]], 'useful_dish_pickup': [[], []], 'dish_drop': [[52, 74, 83, 107], [230]], 'useful_dish_drop': [[52, 74, 83], [230]], 'soup_pickup': [[], []], 'soup_delivery': [[], []], 'soup_drop': [[], []], 'optimal_onion_potting': [[136], []], 'optimal_tomato_potting': [[], []], 'viable_onion_potting': [[136], []], 'viable_tomato_potting': [[], []], 'catastrophic_onion_potting': [[], []], 'catastrophic_tomato_potting': [[], []], 'useless_onion_potting': [[], []], 'useless_tomato_potting': [[], []], 'cumulative_sparse_rewards_by_agent': array([0, 0]), 'cumulative_shaped_rewards_by_agent': array([3, 0])}, 'ep_sparse_r': 0, 'ep_shaped_r': 3, 'ep_sparse_r_by_agent': array([0, 0]), 'ep_shaped_r_by_agent': array([3, 0]), 'ep_length': 400}}]],\n", + " ,\n", + " ,\n", + " ]],\n", " dtype=object)}" ] }, - "execution_count": 14, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# ap_sp: The AgentPair we created earlier\n", + "# ap: The AgentPair we created earlier\n", "# 10: how many times we should run the evaluation since the policy is stochastic\n", - "# 400: environment timestep horizon, \n", - "## should not be necessary is the AgentEvaluator is created with a horizon, but good to have for clarity\n", - "result = ae.evaluate_agent_pair(ap_sp, 10, 400)\n", - "result" + "trajs = ae.evaluate_agent_pair(ap_bc, 10)\n", + "trajs" ] }, { @@ -822,31 +585,17 @@ } ], "source": [ - "result[\"ep_returns\"]" + "trajs[\"ep_returns\"]" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "264f7842", + "execution_count": null, + "id": "48875a68", "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'ap_bc' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[16], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# we can use any AgentPair class, like the ap_bc object we created earlier\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# as we can see the performance is not as good as the self-play agents\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m result \u001b[38;5;241m=\u001b[39m ae\u001b[38;5;241m.\u001b[39mevaluate_agent_pair(\u001b[43map_bc\u001b[49m,\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m400\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'ap_bc' is not defined" - ] - } - ], + "outputs": [], "source": [ - "# we can use any AgentPair class, like the ap_bc object we created earlier\n", - "# as we can see the performance is not as good as the self-play agents\n", - "result = ae.evaluate_agent_pair(ap_bc,10,400)" + "result = ae.evaluate_agent_pair(ap_sp, 1, 400)" ] }, { @@ -861,36 +610,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "464d0c84", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Avg rew: 0.00 (std: 0.00, se: 0.00); avg len: 400.00; : 100%|██████████| 10/10 [00:09<00:00, 1.06it/s]\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "94d22e1843a145fa8f8b6101aab76b49", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "interactive(children=(IntSlider(value=0, description='timestep', max=399), Output()), _dom_classes=('widget-in…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from overcooked_ai_py.visualization.state_visualizer import StateVisualizer\n", - "# here we use the self-play agentPair created earlier again\n", - "trajs = ae.evaluate_agent_pair(ap_sp,10,400)\n", "StateVisualizer().display_rendered_trajectory(trajs, ipython_display=True)" ] }, diff --git a/pyproject.toml b/pyproject.toml index 88ce72f3..f1270167 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,27 +15,25 @@ authors = [ urls = {homepage = "https://github.com/HumanCompatibleAI/overcooked_ai"} keywords = ["Overcooked", "AI", "Reinforcement Learning"] dependencies = [ - "dill>=0.3.9", - "gymnasium>=1.1.1", - "ipywidgets>=8.1.5", - "numpy<=1.23.5", - "opencv-python>=4.11.0.86", - "pygame>=2.6.1", - "scipy>=1.15.2", + "dill", + "gymnasium", + "ipywidgets", + "numpy<2", + "opencv-python", + "pygame", + "scipy", "setuptools>=77.0.3", - "tqdm>=4.67.1", + "tqdm", ] [project.optional-dependencies] harl = [ - "ipykernel>=6.29.5", - "ray[rllib,tune]==2.0", - "sacred>=0.8.7", - "dm-tree>=0.1.9", - "lz4>=4.4.3", - "tensorflow>=2.11.1", - "pydantic<2", - "wandb>=0.18.7", + "ipykernel", + "ray[rllib,tune]==2.2", + "dm-tree", + "lz4", + "tensorflow[and-cuda]==2.19.0", + "wandb", ] [project.scripts] diff --git a/src/human_aware_rl/imitation/reproduce_bc.py b/src/human_aware_rl/imitation/reproduce_bc.py index 83f8c3ed..6e40e6bb 100644 --- a/src/human_aware_rl/imitation/reproduce_bc.py +++ b/src/human_aware_rl/imitation/reproduce_bc.py @@ -1,13 +1,7 @@ import os -from human_aware_rl.imitation.behavior_cloning_tf2 import ( - get_bc_params, - train_bc_model, -) -from human_aware_rl.static import ( - CLEAN_2019_HUMAN_DATA_TEST, - CLEAN_2019_HUMAN_DATA_TRAIN, -) +from human_aware_rl.imitation.behavior_cloning_tf2 import get_bc_params, train_bc_model +from human_aware_rl.static import CLEAN_2019_HUMAN_DATA_TRAIN if __name__ == "__main__": # random 3 is counter_circuit diff --git a/uv.lock b/uv.lock index c56fe6b9..f8503f5c 100644 --- a/uv.lock +++ b/uv.lock @@ -1,7 +1,8 @@ version = 1 requires-python = "==3.10.*" resolution-markers = [ - "sys_platform == 'darwin'", + "platform_machine == 'x86_64' and sys_platform == 'darwin'", + "platform_machine != 'x86_64' and sys_platform == 'darwin'", "platform_machine == 'aarch64' and sys_platform == 'linux'", "platform_machine != 'aarch64' and sys_platform == 'linux'", "sys_platform != 'darwin' and sys_platform != 'linux'", @@ -16,18 +17,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/2f/7a/874c46ad2d14998bc2eedac1133c5299e12fe728d2ce91b4d64f2fcc5089/absl_py-2.2.0-py3-none-any.whl", hash = "sha256:5c432cdf7b045f89c4ddc3bba196cabb389c0c321322f8dec68eecdfa732fdad", size = 276986 }, ] -[[package]] -name = "aiosignal" -version = "1.3.2" -source = { registry = 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++++++++++--------------------- 2 files changed, 24 insertions(+), 45 deletions(-) diff --git a/.github/workflows/pythontests.yml b/.github/workflows/pythontests.yml index b4571727..fc8dca12 100644 --- a/.github/workflows/pythontests.yml +++ b/.github/workflows/pythontests.yml @@ -10,6 +10,10 @@ jobs: with: python-version: 3.10.16 architecture: 'x64' + - name: Install missing dependencies + run: | + brew install gettext + brew link --force gettext - name: Install uv run: | curl -LsSf https://astral.sh/uv/install.sh | sh diff --git a/README.md b/README.md index c75b4861..8fb9459b 100644 --- a/README.md +++ b/README.md @@ -21,8 +21,6 @@ DRL implementations compatible with the environment are included in the repo as The old [human_aware_rl](https://github.com/HumanCompatibleAI/human_aware_rl) is being deprecated and should only used to reproduce the results in the 2019 paper: *[On the Utility of Learning about Humans for Human-AI Coordination](https://arxiv.org/abs/1910.05789)* (also see our [blog post](https://bair.berkeley.edu/blog/2019/10/21/coordination/)). -For simple usage of the environment, it's worthwhile considering using [this environment wrapper](https://github.com/Stanford-ILIAD/PantheonRL). - ## Research Papers using Overcooked-AI 📑 @@ -45,34 +43,20 @@ You can install the pre-compiled wheel file using pip. ``` pip install overcooked-ai ``` -Note that PyPI releases are stable but infrequent. For the most up-to-date development features, build from source with `pip install -e .`. +Note that PyPI releases are stable but infrequent. For the most up-to-date development features, build from source. We recommend using [uv](https://docs.astral.sh/uv/getting-started/installation/) to install the package, so that you can use the provided lockfile to ensure no minimal package version issues. ### Building from source 🔧 -It is useful to setup a conda environment with Python 3.7 (virtualenv works too): - -``` -conda create -n overcooked_ai python=3.7 -conda activate overcooked_ai -``` - Clone the repo ``` git clone https://github.com/HumanCompatibleAI/overcooked_ai.git ``` -Finally, use python setup-tools to locally install - -If you just want to use the environment: +Using uv (recommended): ``` -pip install -e . -``` - -If you also need the DRL implementations (you may have to input this in your terminal as `pip install -e '.[harl]'`): - -``` -pip install -e .[harl] +uv venv +uv sync ``` @@ -84,21 +68,15 @@ When building from source, you can verify the installation by running the Overco python testing/overcooked_test.py ``` -To check whether the `humam_aware_rl` is installed correctly, you can run the following command from the `src/human_aware_rl` directory: - -``` -$ ./run_tests.sh -``` -⚠️**Be sure to change your CWD to the human_aware_rl directory before running the script, as the test script uses the CWD to dynamically generate a path to save temporary training runs/checkpoints. The testing script will fail if not being run from the correct directory.** -This will run all tests belonging to the human_aware_rl module. _These tests don't work anymore out of the box, due to package version issues_: if you fix them, feel free to make a PR. You can checkout the README in the submodule for instructions of running target-specific tests. This can be initiated from any directory. If you're thinking of using the planning code extensively, you should run the full testing suite that verifies all of the Overcooked accessory tools (this can take 5-10 mins): ``` python -m unittest discover -s testing/ -p "*_test.py" ``` +See this [notebook](Overcooked%20Tutorial.ipynb) for a quick guide on getting started using the environment. ## Code Structure Overview 🗺 @@ -117,7 +95,16 @@ python -m unittest discover -s testing/ -p "*_test.py" - `planners.py`: near-optimal agent planning logic - `search.py`: A* search and shortest path logic -`human_aware_rl` contains: +`overcooked_demo` contains: + +`server/`: +- `app.py`: The Flask app +- `game.py`: The main logic of the game. State transitions are handled by overcooked.Gridworld object embedded in the game environment +- `move_agents.py`: A script that simplifies copying checkpoints to [agents](src/overcooked_demo/server/static/assets/agents/) directory. Instruction of how to use can be found inside the file or by running `python move_agents.py -h` + +`up.sh`: Shell script to spin up the Docker server that hosts the game + +`human_aware_rl` contains (NOTE: this is not supported anymore, see bottom of the README for more info): `ppo/`: - `ppo_rllib.py`: Primary module where code for training a PPO agent resides. This includes an rllib compatible wrapper on `OvercookedEnv`, utilities for converting rllib `Policy` classes to Overcooked `Agent`s, as well as utility functions and callbacks @@ -142,29 +129,17 @@ python -m unittest discover -s testing/ -p "*_test.py" `utils.py`: utils for the repo -`overcooked_demo` contains: -`server/`: -- `app.py`: The Flask app -- `game.py`: The main logic of the game. State transitions are handled by overcooked.Gridworld object embedded in the game environment -- `move_agents.py`: A script that simplifies copying checkpoints to [agents](src/overcooked_demo/server/static/assets/agents/) directory. Instruction of how to use can be found inside the file or by running `python move_agents.py -h` - -`up.sh`: Shell script to spin up the Docker server that hosts the game - - -## Python Visualizations 🌠 +## Raw Data :ledger: -See [this Google Colab](https://colab.research.google.com/drive/1AAVP2P-QQhbx6WTOnIG54NXLXFbO7y6n#scrollTo=Z1RBlqADnTDw) for some sample code for visualizing trajectories in python. +The raw data used during BC training is >100 MB, which makes it inconvenient to distribute via git. The code uses pickled dataframes for training and testing, but in case one needs to original data it can be found [here](https://drive.google.com/drive/folders/1aGV8eqWeOG5BMFdUcVoP2NHU_GFPqi57?usp=share_link) -We have incorporated a [notebook](Overcooked%20Tutorial.ipynb) that guides users on the process of training, loading, and evaluating agents. Ideally, we would like to enable users to execute the notebook in Google Colab; however, due to Colab's default kernel being Python 3.10 and our repository being optimized for Python 3.7, some functions are presently incompatible with Colab. To provide a seamless experience, we have pre-executed all the cells in the notebook, allowing you to view the expected output when running it locally following the appropriate setup. +## Deprecated: Behavior Cloning and Reinforcement Learning -Overcooked_demo can also start an interactive game in the browser for visualizations. Details can be found in its [README](src/overcooked_demo/README.md) +We used to include code for training BC and PPO agents in the `human_aware_rl` module. This is now deprecated, because of package version issues which are hard to fix. See this [issue](https://github.com/HumanCompatibleAI/overcooked_ai/issues/162) for more details. -## Raw Data :ledger: -The raw data used in training is >100 MB, which makes it inconvenient to distribute via git. The code uses pickled dataframes for training and testing, but in case one needs to original data it can be found [here](https://drive.google.com/drive/folders/1aGV8eqWeOG5BMFdUcVoP2NHU_GFPqi57?usp=share_link) ## Further Issues and questions ❓ -If you have issues or questions, you can contact [Micah Carroll](https://micahcarroll.github.io) at mdc@berkeley.edu. - +If you have issues or questions, you can contact [Micah Carroll](https://micahcarroll.github.io) at mdc@berkeley.edu. \ No newline at end of file From 89ccbb1b49d87f268baa7a06e7de64802ec86754 Mon Sep 17 00:00:00 2001 From: micah Date: Sat, 22 Mar 2025 12:07:20 -0700 Subject: [PATCH 11/13] Simplifying github actions --- .github/workflows/blackisort.yml | 30 ------------------------------ .github/workflows/pythonlint.yml | 29 ----------------------------- .github/workflows/pythontests.yml | 26 -------------------------- 3 files changed, 85 deletions(-) delete mode 100644 .github/workflows/blackisort.yml delete mode 100644 .github/workflows/pythonlint.yml diff --git a/.github/workflows/blackisort.yml b/.github/workflows/blackisort.yml deleted file mode 100644 index 2313aea2..00000000 --- a/.github/workflows/blackisort.yml +++ /dev/null @@ -1,30 +0,0 @@ -# name: black isort - -# on: [push, pull_request] - -# jobs: -# lint: -# runs-on: ubuntu-latest -# steps: -# - uses: actions/checkout@v3 -# - uses: actions/setup-python@v4 -# with: -# python-version: 3.8 -# - name: Install uv -# run: | -# curl -LsSf https://astral.sh/uv/install.sh | sh -# echo "$HOME/.cargo/bin" >> $GITHUB_PATH -# - name: Setup virtual environment and install black and isort -# run: | -# uv venv -# source .venv/bin/activate -# uv pip install black==23.3.0 isort -# - name: Running Black -# run: | -# source .venv/bin/activate -# black --check --diff --line-length 79 . -# - name: Running isort -# if: always() -# run: | -# source .venv/bin/activate -# isort --check-only --line-length 79 --profile black . diff --git a/.github/workflows/pythonlint.yml b/.github/workflows/pythonlint.yml deleted file mode 100644 index f8b5b53b..00000000 --- a/.github/workflows/pythonlint.yml +++ /dev/null @@ -1,29 +0,0 @@ -# on: -# push: -# branches: "*" -# pull_request: -# branches: "*" -# jobs: -# build: -# runs-on: ubuntu-latest -# steps: -# - uses: actions/checkout@v3 -# - name: Set up Python 3.8 -# uses: actions/setup-python@v4 -# with: -# python-version: 3.10.16 -# architecture: 'x64' -# - name: Install uv -# run: | -# curl -LsSf https://astral.sh/uv/install.sh | sh -# echo "$HOME/.cargo/bin" >> $GITHUB_PATH -# - name: Setup virtual environment and install dependencies -# run: | -# uv venv -# source .venv/bin/activate -# uv pip install flake8 -# - name: Lint with flake8 -# run: | -# source .venv/bin/activate -# # stop the build if there are Python syntax errors or undefined names -# flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics diff --git a/.github/workflows/pythontests.yml b/.github/workflows/pythontests.yml index fc8dca12..e8c99dc0 100644 --- a/.github/workflows/pythontests.yml +++ b/.github/workflows/pythontests.yml @@ -1,31 +1,5 @@ on: ['push', 'pull_request'] jobs: - # Removed macos tests because github actions doesn't support python 3.7 anymore - osx_mdp_tests: - runs-on: macos-latest - steps: - - uses: actions/checkout@v3 - - name: Set up Python 3.10.16 - uses: actions/setup-python@v4 - with: - python-version: 3.10.16 - architecture: 'x64' - - name: Install missing dependencies - run: | - brew install gettext - brew link --force gettext - - name: Install uv - run: | - curl -LsSf https://astral.sh/uv/install.sh | sh - - name: Install dependencies - run: | - uv venv - source .venv/bin/activate - uv pip install -e . - - name: Run tests - run: | - source .venv/bin/activate - coverage run -m unittest discover -s testing/ -p "*_test.py" ubuntu_mdp_tests: runs-on: ubuntu-latest steps: From c85fc4a1814d45d6d6b9d5834824aaccea2f44f6 Mon Sep 17 00:00:00 2001 From: micah Date: Sat, 22 Mar 2025 12:21:11 -0700 Subject: [PATCH 12/13] Updating the README --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 8fb9459b..370e77bc 100644 --- a/README.md +++ b/README.md @@ -17,9 +17,9 @@ The goal of the game is to deliver soups as fast as possible. Each soup requires You can **try out the game [here](https://humancompatibleai.github.io/overcooked-demo/)** (playing with some previously trained DRL agents). To play with your own trained agents using this interface, or to collect more human-AI or human-human data, you can use the code [here](https://github.com/HumanCompatibleAI/overcooked_ai/tree/master/src/overcooked_demo). You can find some human-human and human-AI gameplay data already collected [here](https://github.com/HumanCompatibleAI/overcooked_ai/tree/master/src/human_aware_rl/static/human_data). -DRL implementations compatible with the environment are included in the repo as a submodule under src/human_aware_rl. +**NOTE + LOOKING FOR CONTRIBUTORS:** DRL and BC implementations are now deprecated. We used to include code for training BC and PPO agents in the `human_aware_rl` directory. See [this issue](https://github.com/HumanCompatibleAI/overcooked_ai/issues/162) for more details. -The old [human_aware_rl](https://github.com/HumanCompatibleAI/human_aware_rl) is being deprecated and should only used to reproduce the results in the 2019 paper: *[On the Utility of Learning about Humans for Human-AI Coordination](https://arxiv.org/abs/1910.05789)* (also see our [blog post](https://bair.berkeley.edu/blog/2019/10/21/coordination/)). +This benchmark was build in the context of a 2019 paper: *[On the Utility of Learning about Humans for Human-AI Coordination](https://arxiv.org/abs/1910.05789)*. Also see our [blog post](https://bair.berkeley.edu/blog/2019/10/21/coordination/). ## Research Papers using Overcooked-AI 📑 @@ -136,7 +136,7 @@ The raw data used during BC training is >100 MB, which makes it inconvenient to ## Deprecated: Behavior Cloning and Reinforcement Learning -We used to include code for training BC and PPO agents in the `human_aware_rl` module. This is now deprecated, because of package version issues which are hard to fix. See this [issue](https://github.com/HumanCompatibleAI/overcooked_ai/issues/162) for more details. + From f27d74d28daa9b4867037a66f91474663ac4d661 Mon Sep 17 00:00:00 2001 From: micah Date: Sat, 22 Mar 2025 12:53:21 -0700 Subject: [PATCH 13/13] Reverted overcooked demo changes (didn't seem to be working) --- pyproject.toml | 15 +- src/overcooked_demo/README.md | 26 - src/overcooked_demo/docker-compose.yml | 2 + src/overcooked_demo/server/Dockerfile | 55 +- src/overcooked_demo/server/README.md | 48 - src/overcooked_demo/server/app.py | 3 +- src/overcooked_demo/server/config.json | 45 +- src/overcooked_demo/server/dev_helper.py | 29 - src/overcooked_demo/server/init.sh | 7 - src/overcooked_demo/server/pyproject.toml | 42 - src/overcooked_demo/server/requirements.txt | 7 +- src/overcooked_demo/server/run_server.sh | 48 - src/overcooked_demo/server/uv.lock | 1881 ------------------- src/overcooked_demo/up.sh | 68 +- 14 files changed, 63 insertions(+), 2213 deletions(-) delete mode 100644 src/overcooked_demo/server/README.md delete mode 100644 src/overcooked_demo/server/dev_helper.py delete mode 100644 src/overcooked_demo/server/init.sh delete mode 100644 src/overcooked_demo/server/pyproject.toml delete mode 100755 src/overcooked_demo/server/run_server.sh delete mode 100644 src/overcooked_demo/server/uv.lock diff --git a/pyproject.toml b/pyproject.toml index f1270167..02908bdd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,11 +8,9 @@ version = "2.0.0" description = "Cooperative multi-agent environment based on Overcooked" readme = "README.md" requires-python = ">=3.10,<3.11" -license = {file = "LICENSE"} -authors = [ - {name = "Micah Carroll", email = "mdc@berkeley.edu"} -] -urls = {homepage = "https://github.com/HumanCompatibleAI/overcooked_ai"} +license = { file = "LICENSE" } +authors = [{ name = "Micah Carroll", email = "mdc@berkeley.edu" }] +urls = { homepage = "https://github.com/HumanCompatibleAI/overcooked_ai" } keywords = ["Overcooked", "AI", "Reinforcement Learning"] dependencies = [ "dill", @@ -34,6 +32,7 @@ harl = [ "lz4", "tensorflow[and-cuda]==2.19.0", "wandb", + "gym", ] [project.scripts] @@ -41,8 +40,8 @@ overcooked-demo-up = "overcooked_demo:start_server" overcooked-demo-move = "overcooked_demo:move_agent" [tool.setuptools] -package-dir = {"" = "src"} -packages = {find = {where = ["src"]}} +package-dir = { "" = "src" } +packages = { find = { where = ["src"] } } [tool.setuptools.package-data] "overcooked_ai_py" = [ @@ -60,4 +59,4 @@ packages = {find = {where = ["src"]}} "ppo/trained_example/*/.is_checkpoint", "ppo/trained_example/*/.tune_metadata", "ppo/trained_example/*/checkpoint-500", -] +] diff --git a/src/overcooked_demo/README.md b/src/overcooked_demo/README.md index 6b09fac4..7b4b8728 100644 --- a/src/overcooked_demo/README.md +++ b/src/overcooked_demo/README.md @@ -7,9 +7,6 @@ A web application where humans can play Overcooked with trained AI agents. * [Installation](#installation) * [Usage](#usage) - * [Docker Deployment](#docker-deployment) - * [Local Run with uv](#local-run-with-uv) - * [Command Line](#command-line) * [Dependencies](#dependencies) * [Using Pre-trained Agents](#using-pre-trained-agents) * [Updating](#updating) @@ -20,14 +17,8 @@ A web application where humans can play Overcooked with trained AI agents. Building the server image requires [Docker](https://docs.docker.com/get-docker/) -For local development with uv, you'll need: -- Python 3.7 or later -- [uv](https://github.com/astral-sh/uv) - Install with `pip install uv` - ## Usage -### Docker Deployment - The server can be deployed locally using the driver script included in the repo. To run the production server, use the command ```bash ./up.sh production @@ -45,23 +36,6 @@ In order to kill the production server, run ./down.sh ``` -### Local Run with uv - -To run the server locally using uv without Docker, use: - -```bash -./up.sh local -``` - -This will: -1. Create a virtual environment in the server directory if it doesn't exist -2. Install all dependencies using uv -3. Start the server on port 5000 - -After running this command, navigate to http://localhost:5000 - -### Command Line - You can also start the server via the command line. After installing the `overcooked_ai` via pip, you can start the server by typing ``` diff --git a/src/overcooked_demo/docker-compose.yml b/src/overcooked_demo/docker-compose.yml index 6cffc420..014c6bbd 100644 --- a/src/overcooked_demo/docker-compose.yml +++ b/src/overcooked_demo/docker-compose.yml @@ -1,3 +1,5 @@ +version : '3.7' + services: app: build: diff --git a/src/overcooked_demo/server/Dockerfile b/src/overcooked_demo/server/Dockerfile index ae245c47..456d45d2 100644 --- a/src/overcooked_demo/server/Dockerfile +++ b/src/overcooked_demo/server/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.9-slim +FROM python:3.7-buster ARG BUILD_ENV ARG OVERCOOKED_BRANCH @@ -6,65 +6,38 @@ ARG GRAPHICS WORKDIR /app -# Install uv -RUN pip install uv +# Install non-chai dependencies +COPY ./requirements.txt ./requirements.txt +RUN pip install -r requirements.txt -# Create a virtual environment for uv -RUN uv venv - -# Copy the pyproject.toml for installation -COPY ./pyproject.toml ./pyproject.toml -COPY ./dev_helper.py ./dev_helper.py - -# Install necessary packages for build -RUN apt-get update && apt-get install -y git build-essential +# Install eventlet production server if production build +RUN if [ "$BUILD_ENV" = "production" ] ; then pip install eventlet ; fi -# Copy local overcooked_ai directory instead of git cloning -COPY ./overcooked_ai /overcooked_ai +# Clone chai code +RUN git clone --recursive https://github.com/HumanCompatibleAI/overcooked_ai.git --branch $OVERCOOKED_BRANCH --single-branch /overcooked_ai # Dummy data_dir so things don't break RUN echo "import os; DATA_DIR=os.path.abspath('.')" >> /overcooked_ai/src/human_aware_rl/data_dir.py -# Install server dependencies -RUN . .venv/bin/activate && uv pip install -e . - -# Install a specific version of Ray that's compatible with the overcooked_ai codebase -RUN . .venv/bin/activate && uv pip install "ray[rllib]>=2.5.0" - -# Install the cloned overcooked_ai in development mode -RUN . .venv/bin/activate && uv pip install -e /overcooked_ai - -# Install eventlet production server if production build -RUN if [ "$BUILD_ENV" = "production" ] ; then . .venv/bin/activate && uv pip install "eventlet>=0.39.0" ; fi +# Install chai dependencies +RUN pip install -e '/overcooked_ai[harl]' -# Install needed packages -RUN apt-get update && apt-get install -y \ - libgl1-mesa-dev \ - libglib2.0-0 \ - libsm6 \ - libxext6 \ - libxrender-dev \ - && rm -rf /var/lib/apt/lists/* +RUN apt-get -y update +RUN apt-get install -y libgl1-mesa-dev # Copy over remaining files COPY ./static ./static COPY ./*.py ./ COPY ./graphics/$GRAPHICS ./static/js/graphics.js COPY ./config.json ./config.json -COPY ./init.sh ./init.sh -# Make init script executable -RUN chmod +x ./init.sh -# Create data directories -RUN mkdir -p /app/data/agents # Set environment variables that will be used by app.py ENV HOST 0.0.0.0 ENV PORT 5000 ENV CONF_PATH config.json -# Expose port and run the app, using init.sh as entrypoint +# Do the thing EXPOSE 5000 -ENTRYPOINT ["./init.sh"] -CMD ["uv", "run", "app.py"] +CMD ["python", "-u", "app.py"] diff --git a/src/overcooked_demo/server/README.md b/src/overcooked_demo/server/README.md deleted file mode 100644 index c2ed999e..00000000 --- a/src/overcooked_demo/server/README.md +++ /dev/null @@ -1,48 +0,0 @@ -# Overcooked Demo Server - -This directory contains the server code for the Overcooked AI demo. - -## Running with uv - -The server now uses [uv](https://github.com/astral-sh/uv) for dependency management and running Python code. - -### Prerequisites - -Install uv: - -```bash -pip install uv -``` - -### Running the Server - -You can run the server directly using: - -```bash -uv run app.py -``` - -Alternatively, use the provided shell script: - -```bash -./run_server.sh -``` - -## Docker - -The Dockerfile has been updated to use uv as well. Build and run the Docker container with: - -```bash -# Build the container -docker build -t overcooked-server \ - --build-arg BUILD_ENV=development \ - --build-arg OVERCOOKED_BRANCH=master \ - --build-arg GRAPHICS=js . - -# Run the container -docker run -p 5000:5000 overcooked-server -``` - -## Dependencies - -Dependencies are now managed through the `pyproject.toml` file. If you need to add or modify dependencies, update this file instead of the legacy `requirements.txt`. \ No newline at end of file diff --git a/src/overcooked_demo/server/app.py b/src/overcooked_demo/server/app.py index e9b9abd4..3094f082 100644 --- a/src/overcooked_demo/server/app.py +++ b/src/overcooked_demo/server/app.py @@ -34,8 +34,7 @@ ########### # Read in global config -script_dir = os.path.dirname(os.path.abspath(__file__)) -CONF_PATH = os.getenv("CONF_PATH", os.path.join(script_dir, "config.json")) +CONF_PATH = os.getenv("CONF_PATH", "config.json") with open(CONF_PATH, "r") as f: CONFIG = json.load(f) diff --git a/src/overcooked_demo/server/config.json b/src/overcooked_demo/server/config.json index a6d92e8e..8802f2f7 100644 --- a/src/overcooked_demo/server/config.json +++ b/src/overcooked_demo/server/config.json @@ -1,24 +1,37 @@ { - "logfile" : "overcooked_app.log", - "layouts" : ["cramped_room", "asymmetric_advantages", "coordination_ring", "forced_coordination", "counter_circuit"], - "layout_globals" : { - "NUM_PLAYERS": 2, - "PROP_FEATS_ENCODING": false, - "REW_SHAPING_PARAMS": null, - "max_num_ingredients": 3, - "onion_value": 21, - "tomato_value": 13, - "onion_time": 10, - "tomato_time": 7 - }, - "MAX_GAME_LENGTH" : 900, - "AGENT_DIR" : "/app/data/agents", + "logfile" : "app.log", + "layouts" : ["cramped_room","counter_circuit_o_1order", "cramped_room_tomato", "asymmetric_advantages", "coordination_ring", "forced_coordination", "counter_circuit", "cramped_corridor", "marshmallow_experiment", "long_cook_time", "forced_coordination_tomato", "asymmetric_advantages_tomato", "marshmallow_experiment_coordination", "pipeline", "you_shall_not_pass", "tutorial_3"], "MAX_GAMES" : 10, + "MAX_GAME_LENGTH" : 120, + "AGENT_DIR" : "./static/assets/agents", "MAX_FPS" : 30, "predefined" : { - "layout_name": "cramped_room" + "experimentParams" : { + "layouts" : ["counter_circuit", "cramped_room"], + "gameTime" : 5, + "playerZero" : "human", + "playerOne" : "human", + "randomized" : true, + "dataCollection": "on" + }, + "lobbyWaitTime" : 300000 }, "tutorial" : { - "layout_name": "tutorial" + "tutorialParams" : { + "layouts" : ["tutorial_3", "tutorial_2", "tutorial_1", "tutorial_0"], + "playerZero" : "human", + "playerOne" : "TutorialAI", + "phaseTwoScore" : 34 + }, + "tomato_value" : 13, + "onion_value" : 21 + }, + "layout_globals" : { + "onion_value" : 21, + "tomato_value" : 13, + "onion_time" : 15, + "tomato_time" : 7, + "order_bonus" : 2, + "max_num_ingredients" : 3 } } \ No newline at end of file diff --git a/src/overcooked_demo/server/dev_helper.py b/src/overcooked_demo/server/dev_helper.py deleted file mode 100644 index 9b4c086f..00000000 --- a/src/overcooked_demo/server/dev_helper.py +++ /dev/null @@ -1,29 +0,0 @@ -""" -Helper module to add the parent src directory to the Python path. -This allows importing human_aware_rl and other modules from the main project. - -Usage: - # At the top of your script (e.g., app.py) - import os - import sys - - # Add parent directory to path - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) - - # Now you can import from human_aware_rl - from human_aware_rl.rllib.rllib import load_agent -""" - -import os -import sys -import pathlib - -def add_src_to_path(): - """Add the parent src directory to the Python path.""" - current_dir = pathlib.Path(__file__).parent.absolute() - src_dir = current_dir.parent.parent # ../.. relative to this file - sys.path.insert(0, str(src_dir)) - print(f"Added {src_dir} to Python path") - -# Automatically add to path when imported -add_src_to_path() \ No newline at end of file diff --git a/src/overcooked_demo/server/init.sh b/src/overcooked_demo/server/init.sh deleted file mode 100644 index 0c4f5964..00000000 --- a/src/overcooked_demo/server/init.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash - -# Create the agents directory if it doesn't exist -mkdir -p /app/data/agents - -# Start the application -exec "$@" \ No newline at end of file diff --git a/src/overcooked_demo/server/pyproject.toml b/src/overcooked_demo/server/pyproject.toml deleted file mode 100644 index c3bd2601..00000000 --- a/src/overcooked_demo/server/pyproject.toml +++ /dev/null @@ -1,42 +0,0 @@ -[build-system] -requires = ["setuptools>=61.0"] -build-backend = "setuptools.build_meta" - -[project] -name = "overcooked_server" -version = "1.0.0" -description = "Server for Overcooked AI demo" -requires-python = ">=3.9" -dependencies = [ - "dill", - "numpy<2.0.0", - "certifi>=2020.6.20", - "click>=8.0", - "dnspython>=1.16.0", - "Flask==2.0.3", - "Flask-SocketIO==4.3.2", - "greenlet>=0.4.16", - "itsdangerous==2.0.1", - "Jinja2==3.0.3", - "MarkupSafe==2.0.1", - "monotonic>=1.5", - "python-engineio==3.14.2", - "python-socketio==4.6.1", - "six>=1.15.0", - "Werkzeug==2.0.3", - "requests>=2.23.0", - "protobuf>=3.19", - "eventlet>=0.39.0", - "ray[rllib]>=2.5.0", -] - -[project.optional-dependencies] -tensorflow = [ - "tensorflow>=2.14.0", -] - -[tool.setuptools] -py-modules = [] - -[tool.setuptools.package-dir] -"" = "../.." \ No newline at end of file diff --git a/src/overcooked_demo/server/requirements.txt b/src/overcooked_demo/server/requirements.txt index cce6f648..a9810c76 100644 --- a/src/overcooked_demo/server/requirements.txt +++ b/src/overcooked_demo/server/requirements.txt @@ -3,17 +3,16 @@ click==8.0 dnspython==1.16.0 dill==0.3.2 Flask==2.1.0 -Flask-SocketIO==5.3.0 +Flask-SocketIO==4.3.0 greenlet==0.4.16 itsdangerous==2.0 Jinja2==3.1.0 MarkupSafe==2.0 monotonic==1.5 -python-engineio==4.3.4 -python-socketio==5.7.2 +python-engineio==3.13.0 +python-socketio==4.6.0 six==1.15.0 Werkzeug==2.0.3 tensorflow==2.0.3 requests==2.23.0 protobuf==3.19 -ray[rllib]==1.13.0 diff --git a/src/overcooked_demo/server/run_server.sh b/src/overcooked_demo/server/run_server.sh deleted file mode 100755 index d79b2891..00000000 --- a/src/overcooked_demo/server/run_server.sh +++ /dev/null @@ -1,48 +0,0 @@ -#!/bin/bash - -# This script runs the Overcooked server using uv - -# Check if uv is installed -if ! command -v uv &> /dev/null -then - echo "uv could not be found. Please install it first using 'pip install uv'" - exit 1 -fi - -# Create a venv if it doesn't exist -if [ ! -d ".venv" ]; then - echo "Creating virtual environment..." - uv venv -fi - -# Activate the virtual environment -source .venv/bin/activate - -# Install dependencies if needed -if [ ! -f ".venv/.initialized" ]; then - # Install dependencies from pyproject.toml - echo "Installing dependencies from pyproject.toml..." - uv pip install -e . - - # Also install root package - echo "Ensuring overcooked_ai is installed..." - ROOT_DIR="$(realpath "$(dirname "$0")/../..")" - uv pip install -e "$ROOT_DIR" - - # Try to install tensorflow with a fallback - echo "Attempting to install tensorflow..." - if ! uv pip install -e ".[tensorflow]" 2>/dev/null; then - echo "Could not install latest tensorflow. Trying compatible version..." - # Try a version known to work on many platforms - if ! uv pip install "tensorflow>=2.8.0" 2>/dev/null; then - echo "Warning: Could not install tensorflow. The server will run with limited functionality." - fi - fi - - # Mark as initialized - touch .venv/.initialized -fi - -# Run the server using the Python interpreter from the venv -echo "Starting server..." -.venv/bin/python -m dev_helper app.py "$@" \ No newline at end of file diff --git a/src/overcooked_demo/server/uv.lock b/src/overcooked_demo/server/uv.lock deleted file mode 100644 index 2561e090..00000000 --- a/src/overcooked_demo/server/uv.lock +++ /dev/null @@ -1,1881 +0,0 @@ -version = 1 -requires-python = ">=3.9" -resolution-markers = [ - "python_full_version < '3.10' and platform_machine == 'x86_64' and sys_platform == 'darwin'", - "(python_full_version < '3.10' and platform_machine != 'x86_64') or (python_full_version < '3.10' and sys_platform != 'darwin')", - "python_full_version == '3.10.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'", - "(python_full_version == '3.10.*' and platform_machine != 'x86_64') or (python_full_version == '3.10.*' and sys_platform != 'darwin')", - 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Please install it first using 'pip install uv'" - exit 1 - fi - - # Change to the server directory - cd "$(dirname "$0")/server" - - echo "Installing dependencies..." - # Create a venv if it doesn't exist - if [ ! -d ".venv" ]; then - echo "Creating virtual environment..." - uv venv - fi - - # Activate the virtual environment - source .venv/bin/activate - - # Install dependencies from pyproject.toml - echo "Installing dependencies from pyproject.toml..." - uv pip install -e . - - # Also install root package if it's not already installed - echo "Ensuring overcooked_ai is installed..." - ROOT_DIR="$(realpath "$(dirname "$0")/../..")" - uv pip install -e "$ROOT_DIR" - - # Try to install tensorflow with a fallback - echo "Attempting to install tensorflow..." - if ! uv pip install -e ".[tensorflow]" 2>/dev/null; then - echo "Could not install latest tensorflow. Trying compatible version..." - # Try a version known to work on many platforms - if ! uv pip install "tensorflow>=2.8.0" 2>/dev/null; then - echo "Warning: Could not install tensorflow. The server will run with limited functionality." - echo "You may need to manually install a compatible version of tensorflow." - fi - fi - - echo "Starting server..." - # Use the Python interpreter from the venv to run the app with dev_helper - .venv/bin/python -m dev_helper app.py - - exit 0 -fi - -mkdir -p server/overcooked_ai -cp -r ../../* server/overcooked_ai/ -# Docker mode -if [[ $1 = prod* ]]; then +if [[ $1 = prod* ]]; +then echo "production" export BUILD_ENV=production - # Completely re-build all images from scratch without using build cache - # Copy the overcooked_ai directory from the project root to the server directory - docker compose build --no-cache - docker compose up --force-recreate -d + # Completely re-build all images from scatch without using build cache + docker-compose build --no-cache + docker-compose up --force-recreate -d else echo "development" export BUILD_ENV=development # Uncomment the following line if there has been an update to overcooked-ai code - # docker compose build --no-cache + # docker-compose build --no-cache # Force re-build of all images but allow use of build cache if possible - docker compose up --build + docker-compose up --build fi \ No newline at end of file