pufferlib

Vectorize parallel environments to accelerate reinforcement learning training.

3|Updated Apr 17, 2026

Search Tags:#multi-agent #training #high-performance #reinforcement learning #vectorization

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill pufferlib-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pufferlib
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/pufferlib
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill pufferlib-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

PufferLib addresses the challenge of slow reinforcement learning training, offering a high-performance framework that enables fast parallel environment simulation and training.

Core Features & Use Cases

High-Performance Training: Achieves 1M-4M steps/second training throughput.
Vectorized Environments: Optimizes parallel environment simulation with vectorization.
Multi-Agent Systems: Supports native multi-agent environment setup and training.
Environment Integration: Seamlessly integrates with existing environments from Gymnasium, PettingZoo, and more.
Use Case: Ideal for research and development in reinforcement learning, particularly when working with large-scale multi-agent systems or high-throughput training.

Quick Start

Use the pufferlib skill to train a reinforcement learning agent on the 'procgen-coinrun' environment.

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 for large-scale multi-agent systems?▼

Speed up reinforcement learning training by using high-performance vectorization to optimize parallel environment simulation, achieving 1M-4M steps per second training throughput. This framework is designed specifically for large-scale multi-agent systems and high-throughput tasks.

Does PufferLib work with existing Gymnasium and PettingZoo environments?▼

Yes, PufferLib seamlessly integrates with existing environments from Gymnasium, PettingZoo, and other environment libraries. This allows you to natively set up and train multi-agent systems without rebuilding your simulation environments from scratch.

How do I train a reinforcement learning agent using vectorized environments?▼

Train a reinforcement learning agent by running the quick start script on a standard environment like 'procgen-coinrun'. The framework applies high-performance vectorization to parallelize environment simulation and optimize the overall training pipeline.

What is the best way to run high-throughput parallel training for multi-agent reinforcement learning?▼

The best way to run high-throughput parallel training is using a framework that natively supports multi-agent environment setup and vectorized simulation. This approach optimizes parallel processing to reach millions of steps per second during training.

Do I need PufferLib installed to execute multi-agent training tasks?▼

Yes, you need PufferLib installed to execute these training tasks. It is a required dependency that provides the underlying high-performance vectorization and parallel simulation framework needed to run the reinforcement learning workloads.