MARLlib: Extending RLlib for Multi-agent Reinforcement Learning

Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang, Hao Dong, Zhihui Li, Xiaodan Liang, Yaodong Yang, Xiaojun Chang

Keywords: MARL

TL;DR: We introduce MARLlib, the MARL extension of RLlib

Abstract: Despite the fast development of multi-agent reinforcement learning (MARL) methods, there is a lack of commonly-acknowledged baseline implementation and evaluation platforms. As a result, an urgent need for MARL researchers is to develop an integrated library suite, similar to the role of RLlib in single-agent RL, that delivers reliable MARL implementation and replicable evaluation in various bechmarks. To fill such a research gap, in this paper, we propose Multi-Agent RLlib (MARLlib), a comprehensive MARL algorithm library that facilitates RLlib for solving multi-agent problems. With a novel design of agent-level distributed dataflow, MARLlib manages to unify tens of algorithms, including different types of independent learning, centralized critic, and value decomposition methods; this leads to a highly composable integration of MARL algorithms that are not possible to unify before. Furthermore, MARLlib goes beyond current work by integrating diverse environment interfaces and providing flexible parameter sharing strategies; this allows to create versatile solutions to cooperative, competitive, and mixed tasks with minimal code modifications for end users. A plethora of experiments are conducted to substantiate the correctness of our implementation, based on which we further derive new insights on the relationship between the performance and the design of algorithmic components. With MARLlib, we expect researchers to be able to tackle broader real-world multi-agent problems with trustworthy solutions. Our code\footnote{\url{https://github.com/ICLR2023Paper4242/MARLlib}} and documentation\footnote{\url{https://iclr2023marllib.readthedocs.io/}} are released for reference.

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Please Choose The Closest Area That Your Submission Falls Into: Reinforcement Learning (eg, decision and control, planning, hierarchical RL, robotics)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.13708/code)