sail-sg/envpool

Health v2 · maintenance only

Adoption not part of the score

1506 stars · 141 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

EnvPool is a C++-based batched environment pool with pybind11 bindings and a thread pool for high-performance parallel RL environment execution. It provides Gymnasium and dm_env compatible APIs and ships prebuilt implementations of many popular environments like Atari, MuJoCo, and DeepMind Control Suite.

Use cases

• run reinforcement learning environments at millions of frames per second
• vectorize atari game environments for RL training
• parallelize mujoco physics simulations for RL
• batch dm_control suite environments with async stepping
• speed up RL training data collection pipelines
• replace gym vector envs with a faster C++ backend

When to choose

• you need maximum environment throughput for RL training
• you want prebuilt, optimized implementations of common RL benchmarks like Atari or MuJoCo
• you need Gymnasium or dm_env compatible APIs with sync and async stepping

When to avoid

• you need a custom environment not among the supported list and don't want to write C++
• you only run a handful of environments and don't need vectorization
• you need a pure-Python solution for easy modification of environment logic

Facets

library · maturity active

simulation machine-learning concurrency sdk reinforcement-learning machine-learning simulation robotics python cpp cross-platform reinforcement-learning-environments vectorized-environments gymnasium dm-env pybind11 thread-pool atari mujoco high-performance game-development linux macos

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markdown · JSON · MCP: product_card(name="sail-sg/envpool")