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Ritabrata-Chakraborty
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pufferlib

Train reinforcement learning agents with PufferLib on vectorized environments.

Updated Jun 6, 2026
Search Tags:#optimization#multi-agent#training#reinforcement learning#vectorization
One-click install
npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill pufferlib-ritabrata-chakraborty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pufferlib
Source: https://github.com/Ritabrata-Chakraborty/Claude-Setup/tree/main/skills/pufferlib
Command: npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill pufferlib-ritabrata-chakraborty

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the need for efficient and scalable reinforcement learning (RL) training, providing high-performance tools for parallel environment simulation, vectorization, and integration with popular RL frameworks.

Core Features & Use Cases

  • High-Performance Training: Achieve up to 10x faster training with optimized vectorization and native multi-agent support.
  • Custom Environment Development: Create custom environments with the PufferEnv API, supporting various observation and action spaces.
  • Environment Integration: Seamlessly integrate environments from Gymnasium, PettingZoo, Atari, Procgen, and more.
  • Policy Development: Develop policies using PyTorch, with built-in optimizations for CNN, LSTM, and custom architectures.
  • Use Case: Enhance your RL research with a high-performance toolkit that allows you to quickly prototype and scale complex RL models.

Quick Start

Use the pufferlib skill to train a reinforcement learning agent on the 'procgen-coinrun' environment with PPO. Run: puffer train procgen-coinrun --train.device cuda --train.learning-rate 3e-4

Frequently Asked Questions about pufferlib

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

FAQPage Schema
How do I accelerate reinforcement learning training for multi-agent environments?▼

Vectorize your environments and leverage native multi-agent support to accelerate reinforcement learning training. This optimization enables parallel simulation, achieving up to 10x faster training speeds for complex models.

Can I integrate Gymnasium and PettingZoo environments into a high-performance RL pipeline?▼

Yes, you can seamlessly integrate Gymnasium, PettingZoo, Atari, and Procgen environments into your reinforcement learning pipeline. The framework supports these standards alongside custom environments developed via the PufferEnv API.

How do I train a reinforcement learning agent on Procgen using PPO?▼

Train a reinforcement learning agent on Procgen by running a single command that specifies the environment and hyperparameters. For example, execute `puffer train procgen-coinrun --train.device cuda --train.learning-rate 3e-4` to start.

Does pufferlib support custom policy development with PyTorch architectures?▼

Yes, pufferlib supports policy development using PyTorch. It includes built-in optimizations for CNN, LSTM, and custom architectures to efficiently develop and scale complex reinforcement learning models.

What is the best way to scale parallel training for complex reinforcement learning models?▼

The best way to scale parallel training is using a framework optimized for speed and scale. It provides high-performance vectorization and native multi-agent support to quickly prototype and scale complex reinforcement learning models.

What dependencies do I need to run vectorized reinforcement learning tasks?▼

You need PufferLib and its relevant dependencies installed to execute training tasks. The framework requires these components to run optimized parallel simulations and vectorized reinforcement learning environment integrations.

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