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pufferlib

Scale reinforcement learning experiments with vectorized environments and a persistent agent runtime.

321|26|Updated Mar 10, 2026
Search Tags:#performance#multi-agent#vectorization#reinforcement-learning#ppo#pufferlib#gymnasium
One-click install
npx skills add https://github.com/mkurman/tamux --skill pufferlib-mkurman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pufferlib
Source: https://github.com/mkurman/tamux/tree/main/skills/scientific-skills/pufferlib
Command: npx skills add https://github.com/mkurman/tamux --skill pufferlib-mkurman

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, pufferlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

PufferLib delivers a high-performance reinforcement learning framework to build, train, and deploy agents at scale using a persistent runtime, vectorized environments, and efficient training loops.

Core Features & Use Cases

  • High-throughput training with PuffeRL for single- and multi-agent setups
  • Vectorized environments and Ocean/Gymnasium integration for rapid experimentation
  • Shared utilities for environments, policies, and performance optimization in RL

Quick Start

Launch a minimal experiment by vectorizing a simple environment and training a PPO agent with PuffeRL.

Frequently Asked Questions about pufferlib

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I speed up reinforcement learning training with vectorized environments?▼

Vectorized environments accelerate reinforcement learning by running multiple environment instances simultaneously through a shared memory backend, enabling high-throughput data collection for training loops.

Can I use PufferLib for multi-agent reinforcement learning with PettingZoo?▼

Yes, PufferLib supports both single-agent and multi-agent reinforcement learning, providing integration with PettingZoo and Gymnasium environments for rapid prototyping across various RL setups.

Does PufferLib work with PyTorch for PPO and LSTM training?▼

Yes, PufferLib uses PyTorch as its core dependency and supports PPO and LSTM training algorithms within its persistent runtime to optimize policy networks efficiently.

What is the best way to scale reinforcement learning experiments for high-throughput environments?▼

The best way to scale RL experiments is using a persistent runtime with shared memory vectorization, which maximizes environment throughput and supports distributed training integration hooks for logging.

Do I need PyTorch to run offline-to-online reinforcement learning training?▼

Yes, PyTorch is a required dependency for running PufferLib's offline-to-online training, as the framework relies on it for executing PPO and LSTM training loops.

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