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There has been significant progress in developing reinforcement learning (RL)training systems. Past works such as IMPALA, Apex, Seed RL, Sample Factory, andothers, aim to improve the system's overall throughput. In this paper, we aimto address a common bottleneck in the RL training system, i.e., parallelenvironment execution, which is often the slowest part of the whole system butreceives little attention. With a curated design for paralleling RLenvironments, we have improved the RL environment simulation speed acrossdifferent hardware setups, ranging from a laptop and a modest workstation, to ahigh-end machine such as NVIDIA DGX-A100. On a high-end machine, EnvPoolachieves one million frames per second for the environment execution on Atarienvironments and three million frames per second on MuJoCo environments. Whenrunning EnvPool on a laptop, the speed is 2.8x that of the Python subprocess.Moreover, great compatibility with existing RL training libraries has beendemonstrated in the open-sourced community, including CleanRL, rl_games,DeepMind Acme, etc. Finally, EnvPool allows researchers to iterate their ideasat a much faster pace and has great potential to become the de facto RLenvironment execution engine. Example runs show that it only takes five minutesto train agents to play Atari Pong and MuJoCo Ant on a laptop. EnvPool isopen-sourced at https://github.com/sail-sg/envpool.

EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine