pufferlib

SKILL.md

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 fra...

PuffeRL is PufferLib's optimized PPO+LSTM training algorithm achieving 1M-4M steps/second.

Use the template script: scripts/envtemplate.py provides complete single-agent and multi-agent environment templates with examples of:

This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs. Source: ovachiever/droid-tings.

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