Overcooked-AI

5 of the available layouts. New layouts are easy to hardcode or generate programmatically.

Introduction

Overcooked-AI is a benchmark environment for fully cooperative multi-agent performance, based on the widly popular video game Overcooked.

The goal of the game is to deliver soups as fast as possible. Each soup requires taking 3 items and placing them in a pot, waiting for the soup to cook, and then having an agent pick up the soup and delivering it. The agents should split up tasks on the fly and coordinate effectively in order to achieve high reward.

You can try out the game here (playing with some previously trained DRL agents). To play with your own trained agents using this interface, you can use this repo).

For DRL implementations compatible with the environment and reproducibile results to our paper (On the Utility of Learning about Humans for Human-AI Coordination), check out this repo.

Installation

It is useful to setup a conda environment with Python 3.7:

conda create -n overcooked_ai python=3.7
conda activate overcooked_ai

To complete the installation after cloning the repo, run the following commands:

cd overcooked_ai
python setup.py develop

In overcooked_ai_js there is a javascript implementation of the Overcooked MDP and game visualizer.

To install it, cd into overcooked_ai_js and set up the package with npm install. For development, you will also need to install browserify: npm install -g browserify

Verifying Installation

Python Code

To verify your python installation, you can try running the following command from the inner overcooked_ai_py folder:

python run_tests_fast.py

or alternatively (this can take 5-10 mins): python run_tests_full.py

Javascript Code

Run tests with npm run test. Testing scripts use jest, which exposes a window object, and so npm run build-window should be run before running modified tests.

overcooked-window.js is used for the demo and testing.

Python Code Structure Overview

overcooked_ai_py contains:

mdp/:

overcooked_mdp.py: main Overcooked game logic
overcooked_env.py: environment classes built on top of the Overcooked mdp
layout_generator.py: functions to generate random layouts programmatically

agents/:

agent.py: location of agent classes
benchmarking.py: sample trajectories of agents (both trained and planners) and load various models

planning:

planners.py: near-optimal agent planning logic
search.py: A* search and shortest path logic

run_tests.py: script to run all tests

Python Visualizations

One can adapt a version of this file in order to be able to play games in terminal graphics with custom-defined agents.

Javascript Visualizations

To run a simple demo that plays a trajectory demonstrating the transitions in the game (requires having npm installed):

$ npm run demo

Further Issues and questions

If you have issues or questions, don't hesitate to contact Micah Carroll at mdc@berkeley.edu.

tianjunz / overcooked_ai