pufferlib

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

Choose environment from Ocean suite or compatible framework

Use scripts/train_template.py as starting point

Configure hyperparameters for your task

Run training with CLI or Python script

Monitor with Weights & Biases or Neptune

Refer to references/training.md for optimization

For Creating Custom Environments

Start with scripts/env_template.py

Define observation and action spaces

Implement reset() and step() methods

Test environment locally

Vectorize with pufferlib.emulate() or make()

Refer to references/environments.md for advanced patterns

Optimize with references/vectorization.md if needed

For Policy Development

Choose architecture based on observations:

- Vector observations → MLP policy
- Image observations → CNN policy
- Sequential tasks → LSTM policy
- Complex observations → Multi-input policy

Use layer_init for proper weight initialization

Follow patterns in references/policies.md

Test with environment before full training

For Performance Optimization

Profile current throughput (steps per second)

Check vectorization configuration (num_envs, num_workers)

Optimize environment code (in-place ops, numpy vectorization)

Consider C implementation for critical paths

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

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

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

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

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

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

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

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

Test environments: Validate environment logic before scaling up training

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

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

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