EnvPool
A C++-based high-performance parallel environment execution engine for vectorized reinforcement learning simulations.
What is EnvPool?
EnvPool is a C++-based, high-performance parallel environment execution engine designed for reinforcement learning. It vectorizes environment simulations, allowing multiple environments to run concurrently in batched operations, drastically speeding up RL training loops. It solves the bottleneck of slow environment simulation in traditional Python-based RL setups.
Reinforcement learning researchers and engineers who need to train agents faster, particularly those working with large-scale experiments requiring high-throughput environment simulation. It's also valuable for developers integrating custom environments into accelerated RL pipelines.
Developers choose EnvPool for its exceptional performance gains (up to ~20x speedup), broad compatibility with major RL APIs and libraries, and its general-purpose design that works across various environment types unlike GPU-specific solutions.
Overview
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
Use Cases
Best For
- Accelerating Atari game simulations for deep RL research
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.
