Introduction

MALib is a parallel framework for population-based learning methods including Policy Space Response Oracle, Self-Play, Neural Fictitious Self-Play, etc. which are nested with (multi-agent) reinforcement learning algorithms. MALib provides higher-level abstractions of MARL training paradigms, enabling efficient code reuse and flexible deployments on distributed strategies. The design of MALib also strives to promote the research of other multi-agent learning research, including multi-agent imitation learning and model-based RL.

Overview of the MALib architecture.

Feature Overview

The key features of MALib are listed as follows:

• Key Concepts

• MARL Training Paradigms

• Distributed Strategies

• Environments

Citing MALib

If you use MALib in your work, please cite the accompanying paper.

@inproceedings{zhou2021malib,
    title={MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning},
    author={Zhou, Ming and Wan, Ziyu and Wang, Hanjing and Wen, Muning and Wu, Runzhe and Wen, Ying and Yang, Yaodong and Zhang, Weinan and Wang, Jun},
    booktitle={Preprint},
    year={2021},
    organization={Preprint}
}