gymnax
A JAX-based library providing accelerated reinforcement learning environments with full compatibility to the classic gym API.
What is gymnax?
Gymnax is a JAX-based library that provides accelerated reinforcement learning environments compatible with the OpenAI Gym API. It solves the problem of slow CPU-based environment simulation by leveraging JAX's just-in-time compilation and vectorization capabilities, enabling massively parallel rollouts for faster RL experimentation.
Reinforcement learning researchers and practitioners who need high-throughput environment simulation for tasks like meta-learning, evolutionary optimization, or large-scale policy evaluation, particularly those already working within the JAX ecosystem.
Developers choose Gymnax for its unique combination of full Gym API compatibility with JAX-native acceleration, allowing them to easily port existing workflows while gaining orders-of-magnitude speed improvements through batched and compiled environment execution.
Overview
RL Environments in JAX 🌍
Use Cases
Best For
- Running high-throughput RL experiments with massive environment parallelization
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
