EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine (2206.10558v2)

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

• The paper introduces EnvPool, which significantly increases RL training throughput using a high-performance C++ threadpool executor.
• It achieves simulation speeds up to one million frames per second on Atari and three million on MuJoCo, outperforming existing methods by up to 19.2×.
• It integrates seamlessly with popular RL libraries, enabling rapid experimentation and efficient training of complex agents on diverse hardware.

EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine

The paper "EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine" presents a novel approach to overcoming a key bottleneck in Reinforcement Learning (RL) systems: parallel environment execution. Previous frameworks like IMPALA, Apex, and Seed RL have enhanced training throughput, yet parallel environment execution often remains a limiting factor. This paper introduces EnvPool, which is specifically designed to optimize environment execution and significantly accelerate RL training across various hardware setups.

Key Contributions and Methodology

EnvPool employs a curated design using a C++ threadpool-based executor engine to optimize environment execution. The engine is capable of achieving unmatched throughput on both video game environments like Atari and physics-based simulations such as MuJoCo. On high-end machines, EnvPool achieves simulation speeds of one million frames per second on Atari and three million frames per second on MuJoCo environments, surpassing existing implementations by substantial margins.

EnvPool supports both synchronous and asynchronous execution modes, with its asynchronous model notably increasing throughput, especially when environment execution times vary. It leverages lock-free circular buffers for action and state management, thereby minimizing context switching and memory copy overhead.

Performance Evaluation

The performance benefits of EnvPool are demonstrated across various hardware configurations. Compared to Python subprocess implementations, EnvPool delivers a speedup of 14.9× for Atari and 19.2× for MuJoCo environments on an NVIDIA DGX-A100 with 256 CPU cores. Even on consumer-grade laptops, EnvPool achieves a performance boost of 2.8×.

Integration and Compatibility

Importantly, EnvPool maintains compatibility with existing RL libraries, including CleanRL, rl_games, and DeepMind Acme, allowing seamless integration. Through example runs, the paper shows that training complex agents, such as those for Atari Pong and MuJoCo Ant, can be completed in as little as five minutes on a laptop.

Theoretical and Practical Implications

Theoretically, the development of EnvPool emphasizes the potential shift in RL toward high-throughput environments and large-batch training methodologies. Practically, the ability to rapidly iterate experiments accelerates research cycles, ultimately enhancing the speed and efficacy of AI development.

Future Directions

The paper outlines several promising avenues for future research and application:

• Expanding the range of supported environments, including grid worlds and multi-agent settings.
• Developing cross-platform support, potentially extending to MacOS and Windows.
• Exploring distributed environment execution using remote systems and techniques like gRPC.
• Investigating the use of large-batch training to leverage EnvPool's data generation capabilities.

Conclusion

EnvPool offers a significant improvement over existing RL environment execution engines by reducing bottleneck issues and optimizing throughput. The system's flexibility and compatibility with existing frameworks make it a valuable resource for both researchers and practitioners in the AI field. As RL systems aim to harness the power of high-throughput training, EnvPool positions itself as a pivotal innovation facilitating advancements in this domain.