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FLAIROx/JaxMARL: Multi-Agent Reinforcement Learning with JAX

github.com · 1,020 words · saved by 1 readers

JaxMARL combines ease-of-use with GPU-enabled efficiency, and supports a wide range of commonly used MARL environments as well as popular baseline algorithms. Our aim is for one library that enables thorough evaluation of MARL methods across a wide range of tasks and against relevant baselines. We also introduce SMAX, a vectorised, simplified version of the popular StarCraft Multi-Agent Challenge, which removes the need to run the StarCraft II game engine. For more details, take a look at our blog post or our Colab notebook, which walks through the basic usage. We follow CleanRL's philosophy of providing single file implementations which can be found within the baselines directory. We use Hydra to manage our config files, with specifics explained in each algorithm's README. Most files include wandb logging code, this is disabled by default but can be enabled within the file's config. Environments - Before installing, ensure you have the correct JAX version for your hardware accelerator

JaxMARL Installation | Quick Start | Environments | Algorithms | Citation Multi-Agent Reinforcement Learning in JAX JaxMARL combines ease-of-use with GPU-enabled efficiency, and supports a wide range of commonly used MARL environments as well as popular baseline algorithms. Our aim is for one library that enables thorough evaluation of MARL methods across a wide range of tasks and against relevant baselines. We also introduce SMAX, a vectorised, simplified version of the popular StarCraft Multi-Agent Challenge, which removes the need to run the StarCraft II game engine. For more details, take

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