gymnax
Gymnax integrates JAX acceleration into gym APIs, enhancing speed and efficiency in reinforcement learning environments. Supporting various settings from classic control to bsuite, it employs JAX primitives like 'jit', 'vmap', and 'pmap' for high-throughput experiments. The project offers control over environments, beneficial for meta reinforcement learning and evolutionary optimization, including implementing the Anakin sub-architecture. Speed tests on NVIDIA A100 GPUs illustrate its capabilities, making it suitable for scalable RL experiments. Tutorial resources are available for users to start exploring its features.
