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EnvPool is a fast, asynchronous, and parallel RL environment library designed for scaling reinforcement learning experiments. Developed by SAIL at Singapore, it leverages C++ backend and Python frontend for extremely high-speed environment interaction, supporting thousands of environments running in parallel on a single machine. It's compatible with Gymnasium API and RLlib, making it suitable for scalable training pipelines.
Features
- Supports highly parallelized RL environment execution
- Uses C++ backend for ultra-fast simulation
- Compatible with Gym/Gymnasium and RLlib APIs
- Asynchronous stepping and reset for better throughput
- Supports a variety of classic control, Atari, and custom environments
- Easy integration with existing RL libraries for training
Project Samples
Categories
Reinforcement Learning LibrariesLicense
Apache License V2.0Follow EnvPool
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