MG2L: Meta Multi-Agent Reinforcement Learning

This repository is the implementation of the paper "Meta Learning Task Representation in Multi-Agent Reinforcement Learning: from Global Inference to Local Inference."

Overview

Installation

The source code of MAMujoCo and MPE has been included in this repository, but you still need to install OpenAI gym, mujoco-py, rware and MAgent support.

conda create -n mg2l python=3.8
conda activate mg2l
pip install gym==0.21.0 mujoco_py==2.1.2.14 omegaconf rware==1.0.3

Run experiments

You can run the experiments by the following command:

python train.py --expt=default --algo=mg2l --env=mujoco-cheetah-dir gpu_id=0

The --env flag can be followed with any existing config name in the mg2l/config/algo_config/ directory, and any other config named xx (such as gpu_id) can be passed by xx=value.

Demonstration

Citation