PufferLib - High-Performance Reinforcement Learning

Overview

PufferLib is a high-performance reinforcement learning library designed for fast parallel environment simulation and training. It achieves training at millions of steps per second through optimized vectorization, native multi-agent support, and efficient PPO implementation (PuffeRL). The library provides the Ocean suite of 20+ environments and seamless integration with Gymnasium, PettingZoo, and specialized RL frameworks.

When to Use This Skill

Use this skill when:

• Training RL agents with PPO on any environment (single or multi-agent)
• Creating custom environments using the PufferEnv API
• Optimizing performance for parallel environment simulation (vectorization)
• Integrating existing environments from Gymnasium, PettingZoo, Atari, Procgen, etc.
• Developing policies with CNN, LSTM, or custom architectures
• Scaling RL to millions of steps per second for faster experimentation
• Multi-agent RL with native multi-agent environment support

Core Capabilities