Reverted overcooked demo changes (didn't seem to be working)

Author: micahcarroll

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",
@@ -34,15 +32,16 @@ harl = [
     "lz4",
     "tensorflow[and-cuda]==2.19.0",
     "wandb",
+    "gym",
 ]
 
 [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"]}}
+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",
-] 
+]

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
 
 ```

src/overcooked_demo/docker-compose.yml

@@ -1,3 +1,5 @@
+version : '3.7'
+
 services:
     app:
         build:

src/overcooked_demo/server/Dockerfile

@@ -1,70 +1,43 @@
-FROM python:3.9-slim 
+FROM python:3.7-buster 
 
 ARG BUILD_ENV
 ARG OVERCOOKED_BRANCH
 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"]

src/overcooked_demo/server/README.md

@@ -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)
 

src/overcooked_demo/server/app.py

@@ -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
     }
 }

src/overcooked_demo/server/config.json

@@ -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