PufferLib RL

Acerca de

PufferLib provides a comprehensive framework for scaling reinforcement learning tasks to millions of steps per second. It features the optimized PuffeRL algorithm for PPO training, the PufferEnv API for building custom high-throughput environments, and native support for multi-agent systems. Whether you are integrating standard benchmarks like Gymnasium and PettingZoo or developing complex custom simulations with CNN/LSTM architectures, this skill offers the implementation patterns and performance optimization strategies necessary to minimize training time and maximize experimentation speed.

Características Principales

• High-performance PPO training with PuffeRL achieving 1M-4M steps per second
• 2,066 GitHub stars
• Seamless integration with popular RL frameworks like Gymnasium, PettingZoo, and Atari
• Native support for multi-agent reinforcement learning (MARL) systems
• Custom environment development via the optimized PufferEnv API
• Advanced vectorization techniques using shared memory and zero-copy patterns

Casos de Uso

• Training RL agents at scale with optimized PPO and LSTM architectures
• Creating and benchmarking custom high-performance parallel environments
• Optimizing simulation throughput for complex multi-agent scenarios