pufferlib
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
npx skills add LeonChaoX/qinyan-academic-skills --skill pufferlib --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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/s
- Overview
- When to Use This Skill
- Core Capabilities
- 1. High-Performance Training (PuffeRL)
- 2. Environment Development (PufferEnv)
- 3. Vectorization and Performance
- 4. Policy Development
- 5. Environment Integration
- Quick Start Workflow
- For Training Existing Environments
- For Creating Custom Environments
- For Policy Development
- For Performance Optimization
- Resources
CLI training puffer train procgen-coinrun --train.device cuda --train.learning-rate 3e-4 Distributed training torchrun --nproc_per_node=4 train.py uv pip install pufferlib
What does the pufferlib skill do?
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-skills --skill pufferlib --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From LeonChaoX/qinyan-academic-skills, a repository with 759 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.