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Benchmarks are crucial in the development of machine learning algorithms,with available environments significantly influencing reinforcement learning(RL) research. Traditionally, RL environments run on the CPU, which limitstheir scalability with typical academic compute. However, recent advancementsin JAX have enabled the wider use of hardware acceleration, enabling massivelyparallel RL training pipelines and environments. While this has beensuccessfully applied to single-agent RL, it has not yet been widely adopted formulti-agent scenarios. In this paper, we present JaxMARL, the firstopen-source, Python-based library that combines GPU-enabled efficiency withsupport for a large number of commonly used MARL environments and popularbaseline algorithms. Our experiments show that, in terms of wall clock time,our JAX-based training pipeline is around 14 times faster than existingapproaches, and up to 12500x when multiple training runs are vectorized. Thisenables efficient and thorough evaluations, potentially alleviating theevaluation crisis in the field. We also introduce and benchmark SMAX, aJAX-based approximate reimplementation of the popular StarCraft Multi-AgentChallenge, which removes the need to run the StarCraft II game engine. This notonly enables GPU acceleration, but also provides a more flexible MARLenvironment, unlocking the potential for self-play, meta-learning, and otherfuture applications in MARL. The code is available athttps://github.com/flairox/jaxmarl.

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX