README.md

Training MultiGrid agents with RLlib

MultiGrid is compatible with RLlib's multi-agent API.

This folder provides scripts to train and visualize agents over MultiGrid environments.

Requirements

Using MultiGrid environments with RLlib requires installation of rllib, and one of PyTorch or TensorFlow.

Getting Started

Train 2 agents on the MultiGrid-Empty-8x8-v0 environment using the PPO algorithm:

python train.py --algo PPO --env MultiGrid-Empty-8x8-v0 --num-agents 2 --save-dir ~/saved/empty8x8/

Visualize behavior from trained agents policies:

python visualize.py --algo PPO --env MultiGrid-Empty-8x8-v0 --num-agents 2 --load-dir ~/saved/empty8x8/

For more options, run python train.py --help and python visualize.py --help.

Environments

All of the environment configurations registered in multigrid.envs can also be used with RLlib, and are registered via import multigrid.rllib.

To use a specific MultiGrid environment configuration by name:

>>> import multigrid.rllib
>>> from ray.rllib.algorithms.ppo import PPOConfig
>>> algorithm_config = PPOConfig().environment(env='MultiGrid-Empty-8x8-v0')

To convert a custom MultiGridEnv to an RLlib MultiAgentEnv:

>>> from multigrid.rllib import to_rllib_env
>>> MyRLLibEnvClass = to_rllib_env(MyEnvClass)
>>> algorithm_config = PPOConfig().environment(env=MyRLLibEnvClass)