# marl-jax: Multi-Agent Reinforcement Leaning Framework

arXiv: https://arxiv.org/abs/2303.13808 · HTML: https://mlsanity.com/paper/2303.13808/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [The StarCraft Multi-Agent Challenge](https://mlsanity.com/paper/1902.04043/) (2019)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks](https://mlsanity.com/paper/2006.07869/) (2020)
- [Open-Ended Learning Leads to Generally Capable Agents](https://mlsanity.com/paper/2107.12808/) (2021)
- [Acme: A Research Framework for Distributed Reinforcement Learning](https://mlsanity.com/paper/2006.00979/) (2020)
- [Dopamine: A Research Framework for Deep Reinforcement Learning](https://mlsanity.com/paper/1812.06110/) (2018)
- [SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2212.07489/) (2022)
- [Melting Pot 2.0](https://mlsanity.com/paper/2211.13746/) (2022)
- [Podracer architectures for scalable Reinforcement Learning](https://mlsanity.com/paper/2104.06272/) (2021)
- [Reverb: A Framework For Experience Replay](https://mlsanity.com/paper/2102.04736/) (2021)
- [MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2106.07551/) (2021)
- [Generalization in Cooperative Multi-Agent Systems](https://mlsanity.com/paper/2202.00104/) (2022)
- [Mava: a research library for distributed multi-agent reinforcement learning in JAX](https://mlsanity.com/paper/2107.01460/) (2021)
- [Launchpad: A Programming Model for Distributed Machine Learning Research](https://mlsanity.com/paper/2106.04516/) (2021)
- [PantheonRL: A MARL Library for Dynamic Training Interactions](https://mlsanity.com/paper/2112.07013/) (2021)
- [Options as responses: Grounding behavioural hierarchies in multi-agent RL](https://mlsanity.com/paper/1906.01470/) (2019)

## Built on by (most cited first, 1 of 1)

- [Alpha Elimination: Using Deep Reinforcement Learning to Reduce Fill-In during Sparse Matrix Decomposition](https://mlsanity.com/paper/2310.09852/) (2023)

## Abstract (arXiv)

Recent advances in Reinforcement Learning (RL) have led to many exciting applications. These advancements have been driven by improvements in both algorithms and engineering, which have resulted in faster training of RL agents. We present marl-jax, a multi-agent reinforcement learning software package for training and evaluating social generalization of the agents. The package is designed for training a population of agents in multi-agent environments and evaluating their ability to generalize to diverse background agents. It is built on top of DeepMind's JAX ecosystem~citedeepmind2020jax and leverages the RL ecosystem developed by DeepMind. Our framework marl-jax is capable of working in cooperative and competitive, simultaneous-acting environments with multiple agents. The package offers an intuitive and user-friendly command-line interface for training a population and evaluating its generalization capabilities. In conclusion, marl-jax provides a valuable resource for researchers interested in exploring social generalization in the context of MARL. The open-source code for marl-jax is available at: hrefhttps://github.com/kinalmehta/marl-jaxhttps://github.com/kinalmehta/marl-jax