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	null

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()  # Collect rollouts
    trainer.train()     # Train on batch
    trainer.mean_and_log()  # Log results

For comprehensive training guidance, read references/training.md for:

• Complete training workflow and CLI options
• Hyperparameter tuning with Protein
• Distributed multi-GPU/multi-node training
• Logger integration (Weights & Biases, Neptune)
• Checkpointing and resume training
• Performance optimization tips
• Curriculum learning patterns

2. Environment Development (PufferEnv)

Create custom high-performance environments with the PufferEnv API.

Basic environment structure:

import numpy as np
from pufferlib import PufferEnv

class MyEnvironment(PufferEnv):
    def __init__(self, buf=None):
        super().__init__(buf)

        # Define spaces
        self.observation_space = self.make_space((4,))
        self.action_space = self.make_discrete(4)

        self.reset()

    def reset(self):
        # Reset state and return initial observation
        return np.zeros(4, dtype=np.float32)

    def step(self, action):
        # Execute action, compute reward, check done
        obs = self._get_observation()
        reward = self._compute_reward()
        done = self._is_done()
        info = {}

        return obs, reward, done, info

Use the template script: scripts/env_template.py provides complete single-agent and multi-agent environment templates with examples of:

• Different observation space types (vector, image, dict)
• Action space variations (discrete, continuous, multi-discrete)
• Multi-agent environment structure
• Testing utilities

For complete environment development, read references/environments.md for:

• PufferEnv API details and in-place operation patterns
• Observation and action space definitions
• Multi-agent environment creation
• Ocean suite (20+ pre-built environments)
• Performance optimization (Python to C workflow)
• Environment wrappers and best practices
• Debugging and validation techniques

3. Vectorization and Performance

Achieve maximum throughput with optimized parallel simulation.

Vectorization setup:

import pufferlib

# Automatic vectorization
env = pufferlib.make('environment_name', num_envs=256, num_workers=8)

# Performance benchmarks:
# - Pure Python envs: 100k-500k SPS
# - C-based envs: 100M+ SPS
# - With training: 400k-4M total SPS

Key optimizations:

• Shared memory buffers for zero-copy observation passing
• Busy-wait flags instead of pipes/queues
• Surplus environments for async returns
• Multiple environments per worker

For vectorization optimization, read references/vectorization.md for:

• Architecture and performance characteristics
• Worker and batch size configuration
• Serial vs multiprocessing vs async modes
• Shared memory and zero-copy patterns
• Hierarchical vectorization for large scale
• Multi-agent vectorization strategies
• Performance profiling and troubleshooting

4. Policy Development

Build policies as standard PyTorch modules with optional utilities.

Basic policy structure:

import torch.nn as nn
from pufferlib.pytorch import layer_init

class Policy(nn.Module):
    def __init__(self, observation_space, action_space):
        super().__init__()

        # Encoder
        self.encoder = nn.Sequential(
            layer_init(nn.Linear(obs_dim, 256)),
            nn.ReLU(),
            layer_init(nn.Linear(256, 256)),
            nn.ReLU()
        )

        # Actor and critic heads
        self.actor = layer_init(nn.Linear(256, num_actions), std=0.01)
        self.critic = layer_init(nn.Linear(256, 1), std=1.0)

    def forward(self, observations):
        features = self.encoder(observations)
        return self.actor(features), self.critic(features)

For complete policy development, read references/policies.md for:

• CNN policies for image observations
• Recurrent policies with optimized LSTM (3x faster inference)
• Multi-input policies for complex observations
• Continuous action policies
• Multi-agent policies (shared vs independent parameters)
• Advanced architectures (attention, residual)
• Observation normalization and gradient clipping
• Policy debugging and testing

5. Environment Integration

Seamlessly integrate environments from popular RL frameworks.

Gymnasium integration:

import gymnasium as gym
import pufferlib

# Wrap Gymnasium environment
gym_env = gym.make('CartPole-v1')
env = pufferlib.emulate(gym_env, num_envs=256)

# Or use make directly
env = pufferlib.make('gym-CartPole-v1', num_envs=256)

PettingZoo multi-agent:

# Multi-agent environment
env = pufferlib.make('pettingzoo-knights-archers-zombies', num_envs=128)

Supported frameworks:

• Gymnasium / OpenAI Gym
• PettingZoo (parallel and AEC)
• Atari (ALE)
• Procgen
• NetHack / MiniHack
• Minigrid
• Neural MMO
• Crafter
• GPUDrive
• MicroRTS
• Griddly
• And more...

For integration details, read references/integration.md for:

• Complete integration examples for each framework
• Custom wrappers (observation, reward, frame stacking, action repeat)
• Space flattening and unflattening
• Environment registration
• Compatibility patterns
• Performance considerations
• Integration debugging

Quick Start Workflow

For Training Existing Environments

1. Choose environment from Ocean suite or compatible framework
2. Use scripts/train_template.py as starting point
3. Configure hyperparameters for your task
4. Run training with CLI or Python script
5. Monitor with Weights & Biases or Neptune
6. Refer to references/training.md for optimization

For Creating Custom Environments

1. Start with scripts/env_template.py
2. Define observation and action spaces
3. Implement reset() and step() methods
4. Test environment locally
5. Vectorize with pufferlib.emulate() or make()
6. Refer to references/environments.md for advanced patterns
7. Optimize with references/vectorization.md if needed

For Policy Development

1. Choose architecture based on observations:
   • Vector observations → MLP policy
   • Image observations → CNN policy
   • Sequential tasks → LSTM policy
   • Complex observations → Multi-input policy
2. Use layer_init for proper weight initialization
3. Follow patterns in references/policies.md
4. Test with environment before full training

For Performance Optimization

1. Profile current throughput (steps per second)
2. Check vectorization configuration (num_envs, num_workers)
3. Optimize environment code (in-place ops, numpy vectorization)
4. Consider C implementation for critical paths
5. Use references/vectorization.md for systematic optimization

Resources

scripts/

train_template.py - Complete training script template with:

• Environment creation and configuration
• Policy initialization
• Logger integration (WandB, Neptune)
• Training loop with checkpointing
• Command-line argument parsing
• Multi-GPU distributed training setup

env_template.py - Environment implementation templates:

• Single-agent PufferEnv example (grid world)
• Multi-agent PufferEnv example (cooperative navigation)
• Multiple observation/action space patterns
• Testing utilities

references/

training.md - Comprehensive training guide:

• Training workflow and CLI options
• Hyperparameter configuration
• Distributed training (multi-GPU, multi-node)
• Monitoring and logging
• Checkpointing
• Protein hyperparameter tuning
• Performance optimization
• Common training patterns
• Troubleshooting

environments.md - Environment development guide:

• PufferEnv API and characteristics
• Observation and action spaces
• Multi-agent environments
• Ocean suite environments
• Custom environment development workflow
• Python to C optimization path
• Third-party environment integration
• Wrappers and best practices
• Debugging

vectorization.md - Vectorization optimization:

• Architecture and key optimizations
• Vectorization modes (serial, multiprocessing, async)
• Worker and batch configuration
• Shared memory and zero-copy patterns
• Advanced vectorization (hierarchical, custom)
• Multi-agent vectorization
• Performance monitoring and profiling
• Troubleshooting and best practices

policies.md - Policy architecture guide:

• Basic policy structure
• CNN policies for images
• LSTM policies with optimization
• Multi-input policies
• Continuous action policies
• Multi-agent policies
• Advanced architectures (attention, residual)
• Observation processing and unflattening
• Initialization and normalization
• Debugging and testing

integration.md - Framework integration guide:

• Gymnasium integration
• PettingZoo integration (parallel and AEC)
• Third-party environments (Procgen, NetHack, Minigrid, etc.)
• Custom wrappers (observation, reward, frame stacking, etc.)
• Space conversion and unflattening
• Environment registration
• Compatibility patterns
• Performance considerations
• Debugging integration

Tips for Success

1. Start simple: Begin with Ocean environments or Gymnasium integration before creating custom environments

2. Profile early: Measure steps per second from the start to identify bottlenecks

3. Use templates: scripts/train_template.py and scripts/env_template.py provide solid starting points

4. Read references as needed: Each reference file is self-contained and focused on a specific capability

5. Optimize progressively: Start with Python, profile, then optimize critical paths with C if needed

6. Leverage vectorization: PufferLib's vectorization is key to achieving high throughput

7. Monitor training: Use WandB or Neptune to track experiments and identify issues early

8. Test environments: Validate environment logic before scaling up training

9. Check existing environments: Ocean suite provides 20+ pre-built environments

10. Use proper initialization: Always use layer_init from pufferlib.pytorch for policies

Common Use Cases

Training on Standard Benchmarks

# Atari
env = pufferlib.make('atari-pong', num_envs=256)

# Procgen
env = pufferlib.make('procgen-coinrun', num_envs=256)

# Minigrid
env = pufferlib.make('minigrid-empty-8x8', num_envs=256)

Multi-Agent Learning

# PettingZoo
env = pufferlib.make('pettingzoo-pistonball', num_envs=128)

# Shared policy for all agents
policy = create_policy(env.observation_space, env.action_space)
trainer = PuffeRL(env=env, policy=policy)

Custom Task Development

# Create custom environment
class MyTask(PufferEnv):
    # ... implement environment ...

# Vectorize and train
env = pufferlib.emulate(MyTask, num_envs=256)
trainer = PuffeRL(env=env, policy=my_policy)

High-Performance Optimization

# Maximize throughput
env = pufferlib.make(
    'my-env',
    num_envs=1024,      # Large batch
    num_workers=16,     # Many workers
    envs_per_worker=64  # Optimize per worker
)

Installation

uv pip install pufferlib

Documentation

• Official docs: https://puffer.ai/docs.html
• GitHub: https://github.com/PufferAI/PufferLib
• Discord: Community support available