pufferlib

Orchestrate vectorized reinforcement learning environments and training loops.

22|4|Updated May 25, 2026

Search Tags:#multi-agent #environment #training #vectorization #reinforcement-learning #pufferlib

Ready to run

npx skills add https://github.com/crazymsn/academic-skills --skill pufferlib-crazymsn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pufferlib
Source: https://github.com/crazymsn/academic-skills/tree/main/academic-skills/pufferlib
Command: npx skills add https://github.com/crazymsn/academic-skills --skill pufferlib-crazymsn

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Automates scalable reinforcement learning experimentation by orchestrating vectorized environments and efficient training loops.

Core Features & Use Cases

High-performance vectorized environments with PuffeRL trainer for fast data collection
Native support for single-agent and multi-agent setups via PufferEnv
Seamless integration with Ocean, Gymnasium, PettingZoo, and third-party environments for rapid task prototyping

Quick Start

Install PufferLib and run the training_template.py to start a vectorized training run

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?▼

Speed up reinforcement learning training by orchestrating high-performance vectorized environments with the PuffeRL trainer for fast data collection. This automates scalable experimentation across single-agent and multi-agent tasks using PufferEnv.

Can I use PufferLib with Gymnasium and PettingZoo environments?▼

Yes, PufferLib provides seamless integration with Gymnasium and PettingZoo, alongside Ocean and third-party environments. This enables rapid task prototyping for both single-agent and multi-agent reinforcement learning setups.

What do I need to set up multi-agent reinforcement learning pipelines?▼

Setting up multi-agent reinforcement learning pipelines requires PyTorch, NumPy, and the PufferLib core. These dependencies provide the foundational environments, vectorization, PuffeRL trainer, and policy templates needed for scalable training.

What is the best way to start a vectorized training run for RL experiments?▼

The best way to start a vectorized training run is to install PufferLib and execute the training_template.py script. This automates the orchestration of vectorized environments and efficient training loops for rapid experimentation.

Does PufferLib support both single-agent and multi-agent reinforcement learning setups?▼

Yes, PufferLib offers native support for both single-agent and multi-agent reinforcement learning setups via PufferEnv. It orchestrates efficient training loops to accelerate data collection across diverse task configurations.