Agent skill · research science · aiskillstore
pufferlib
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.
Why this skill is useful
Adds executable scripts and specialized knowledge for high-performance reinforcement learning tasks that the AI wouldn't reliably generate on its own.
What it needs
Requires torch installed locally. About 6k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 1. High-Performance Training (PuffeRL) PuffeRL is PufferLib's optimized PPO+LSTM training algorithm achieving 1M-4M steps/second. Quick start training: Python training loop: For comprehensive training guidance, read references/training.md for: Complete training workflow and CLI options Hyperparameter tuning with Protein Distributed multi-GPU/multi-node training Logger integration (Weights & Biases, Neptune) Checkpointing and resume training Performance optimization tips Curriculum learning patterns 2. Environment Development (PufferEnv) Create custom high-performance environments with the PufferEnv API. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/pufferlib