Does this skill support multi-agent reinforcement learning?

Absolutely. PufferLib has native support for multi-agent systems and includes specific templates and patterns for multi-agent environment development and policy training.

How does PufferLib achieve such high performance?

It utilizes optimized vectorization, shared memory buffers for zero-copy data passing, and efficient C-based backends for environment simulation to minimize overhead.

What policy architectures can I build with this skill?

You can develop standard MLP policies, CNNs for image-based observations, and optimized LSTMs for tasks requiring sequential memory, all within a PyTorch-compatible workflow.

Can I use PufferLib with Gymnasium environments?

Yes, PufferLib provides seamless integration and emulation for Gymnasium, PettingZoo, and many other popular RL frameworks to boost their simulation speed.

PufferLib is a high-performance reinforcement learning library designed for fast parallel environment simulation and optimized training loops, achieving millions of steps per second.

PufferLib High-Performance RL

Name: PufferLib High-Performance RL
Author: x-cmd

byx-cmd

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Data Science & ML

Accelerates reinforcement learning development with high-speed parallel environment simulation and optimized PPO training.

About

PufferLib is a specialized skill for Claude Code that enables developers to build, train, and scale reinforcement learning agents at millions of steps per second. It provides a comprehensive toolkit for creating custom environments using the PufferEnv API, optimizing parallel simulations through efficient vectorization, and implementing advanced policy architectures like CNNs and LSTMs. Whether you are integrating existing Gymnasium or PettingZoo environments or developing a high-performance multi-agent system from scratch, this skill provides the domain-specific guidance and implementation patterns needed to maximize training throughput and experiment iteration speed.

Key Features

8 GitHub stars
Custom environment development via PufferEnv with native multi-agent support
Seamless integration with Gymnasium, PettingZoo, Atari, and Procgen frameworks
High-performance PuffeRL (PPO+LSTM) training achieving up to 4M steps per second
Optimized vectorization using shared memory and zero-copy observation passing
Advanced policy development for CNN, LSTM, and multi-input architectures

Use Cases

Converting existing Gymnasium environments into high-throughput vectorized simulations for faster research
Training multi-agent reinforcement learning models for complex competitive or cooperative games
Developing custom RL environments that require C-level performance with Python-level ease of use

About

Key Features

8 GitHub stars
Custom environment development via PufferEnv with native multi-agent support
Seamless integration with Gymnasium, PettingZoo, Atari, and Procgen frameworks
High-performance PuffeRL (PPO+LSTM) training achieving up to 4M steps per second
Optimized vectorization using shared memory and zero-copy observation passing
Advanced policy development for CNN, LSTM, and multi-input architectures

Use Cases

Converting existing Gymnasium environments into high-throughput vectorized simulations for faster research
Training multi-agent reinforcement learning models for complex competitive or cooperative games
Developing custom RL environments that require C-level performance with Python-level ease of use