pufferlib

name	pufferlib
description	High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
license	MIT license
metadata	{"skill-author":"K-Dense Inc."}
verified	false
lastVerifiedAt	"2026-02-19T05:29:09.098Z"
source	builtin
trust_score	100
provenance_sha	a36766c85bdbf0ab

PufferLib - High-Performance Reinforcement Learning

Overview

PufferLib is a high-performance reinforcement learning library designed for fast parallel environment simulation and training. It achieves training at millions of steps per second through optimized vectorization, native multi-agent support, and efficient PPO implementation (PuffeRL). The library provides the Ocean suite of 20+ environments and seamless integration with Gymnasium, PettingZoo, and specialized RL frameworks.

When to Use This Skill

Use this skill when:

• Training RL agents with PPO on any environment (single or multi-agent)
• Creating custom environments using the PufferEnv API
• Optimizing performance for parallel environment simulation (vectorization)
• Integrating existing environments from Gymnasium, PettingZoo, Atari, Procgen, etc.
• Developing policies with CNN, LSTM, or custom architectures
• Scaling RL to millions of steps per second for faster experimentation
• Multi-agent RL with native multi-agent environment support

Core Capabilities

1. High-Performance Training (PuffeRL)

PuffeRL is PufferLib's optimized PPO+LSTM training algorithm achieving 1M-4M steps/second.

Quick start training:

# CLI training
puffer train procgen-coinrun --train.device cuda --train.learning-rate 3e-4

# Distributed training
torchrun --nproc_per_node=4 train.py

Python training loop:

import pufferlib
from pufferlib import PuffeRL

# Create vectorized environment
env = pufferlib.make('procgen-coinrun', num_envs=256)

# Create trainer
trainer = PuffeRL(
    env=env,
    policy=my_policy,
    device='cuda',
    learning_rate=3e-4,
    batch_size=32768
)

# Training loop
for iteration in range(num_iterations):
    trainer.evaluate()  
    trainer.train()     
    trainer.mean_and_log()