Is PufferLib compatible with OpenAI Gymnasium?

Yes, PufferLib provides seamless wrappers and integration for Gymnasium, PettingZoo, Atari, and several other popular RL frameworks, allowing you to use existing environments with its high-speed trainer.

What is the primary advantage of using PufferLib?

PufferLib focuses on performance, allowing users to achieve training speeds of 1M-4M steps per second through optimized vectorization and a high-performance PPO implementation called PuffeRL.

Does PufferLib support distributed training?

Yes, PufferLib supports multi-GPU and multi-node distributed training using torchrun, making it suitable for large-scale RL experiments.

Does this skill help with custom environment creation?

Absolutely. It includes templates and best practices for the PufferEnv API, guiding you through defining observation spaces, action spaces, and optimized reset/step logic.

PufferLib Reinforcement Learning

Name: PufferLib Reinforcement Learning
Author: pur3v4d3r

bypur3v4d3r

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Data Science & ML

Builds and trains high-performance reinforcement learning agents using optimized vectorization and multi-agent simulation.

About

PufferLib is a specialized skill for Claude Code designed to streamline the development and training of reinforcement learning (RL) models. It enables developers to achieve massive training throughput—up to millions of steps per second—through optimized parallel simulation and the efficient PuffeRL trainer. Whether you are building custom environments with the PufferEnv API, integrating standard Gymnasium or PettingZoo tasks, or scaling policies with CNNs and LSTMs, this skill provides the domain-specific guidance and implementation patterns needed for professional-grade RL experimentation and optimization.

Key Features

High-speed PPO training with the optimized PuffeRL algorithm
Seamless integration with Gymnasium, PettingZoo, Atari, and Procgen
1 GitHub stars
Native multi-agent system support for complex cooperative or competitive tasks
Massively parallel environment vectorization for maximum throughput
Optimized policy architectures including CNN, LSTM, and multi-input modules

Use Cases

Developing custom high-performance environments using the PufferEnv API
Scaling multi-agent simulations across distributed GPU and CPU nodes
Training RL agents for games or simulations at millions of steps per second

About

Key Features

High-speed PPO training with the optimized PuffeRL algorithm
Seamless integration with Gymnasium, PettingZoo, Atari, and Procgen
1 GitHub stars
Native multi-agent system support for complex cooperative or competitive tasks
Massively parallel environment vectorization for maximum throughput
Optimized policy architectures including CNN, LSTM, and multi-input modules

Use Cases

Developing custom high-performance environments using the PufferEnv API
Scaling multi-agent simulations across distributed GPU and CPU nodes
Training RL agents for games or simulations at millions of steps per second