PufferLib Reinforcement Learning

关于

The PufferLib skill enables Claude Code to handle sophisticated reinforcement learning (RL) tasks with extreme efficiency. It leverages the PufferLib library to achieve training speeds of millions of steps per second through optimized vectorization and native multi-agent support. This skill is ideal for developers and researchers building custom environments via the PufferEnv API, training agents with PPO (PuffeRL), or integrating existing frameworks like Gymnasium and PettingZoo into high-throughput discovery workflows.

主要功能

• PufferEnv API for developing custom single and multi-agent environments
• 324 GitHub stars
• Support for complex policy architectures including CNNs, LSTMs, and multi-input models
• High-performance PPO+LSTM training (PuffeRL) reaching 4M+ steps per second
• Optimized vectorization for parallel environment simulation with zero-copy buffers
• Deep integration with Gymnasium, PettingZoo, Atari, and Procgen frameworks

使用场景

• Training RL agents for complex gaming or simulation environments at scale
• Creating high-throughput custom environments for autonomous scientific discovery
• Optimizing and vectorizing existing Gymnasium or PettingZoo environments for faster training