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.
npx skills add majiayu000/claude-skill-registry --skill scientific-pkg-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 pip install pufferlib
What does the pufferlib skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scientific-pkg-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 majiayu000/claude-skill-registry, a repository with 534 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.