JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Understand

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 limits their scalability with typical academic compute.
• However, recent advancements in JAX have enabled the wider use of hardware acceleration, enabling massively parallel RL training pipelines and environments.
• While this has been successfully applied to single-agent RL, it has not yet been widely adopted for multi-agent scenarios.