alterlab-pufferlib

name	alterlab-pufferlib
description	Scales reinforcement learning with PufferLib — high-throughput parallel training (PuffeRL), vectorized environments, and native multi-agent systems achieving 2-10x speedups over standard implementations. Use when scaling RL to millions of steps per second, running vectorized or multi-agent setups, building custom PufferEnv tasks, or integrating game environments (Atari, Procgen, NetHack, PettingZoo). For standard single-agent algorithm implementations (PPO/SAC/DQN) or quick prototyping prefer alterlab-stable-baselines3. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Write Edit Bash(python:*) Bash(uv:*)
compatibility	No API key required. Runs locally via `uv run python`; requires the pufferlib Python package (GPU optional for faster training). Targets PufferLib 3.0.x (the current PyPI release); the dev `4.0` branch has a different, unstable API.
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

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 trainer (CleanRL-derived, with optional LSTM via models.LSTMWrapper) built for high-throughput training.

Recommended path — CLI / high-level helper. Drive training from a config (an .ini in pufferlib/config/) rather than hand-wiring the trainer:

# CLI: env name resolves to a registered config + Ocean env
puffer train puffer_breakout --train.device cuda --train.learning-rate 0.015

import pufferlib.pufferl as pufferl
# train(env_name, args=None, vecenv=None, policy=None, logger=None)
pufferl.train('puffer_breakout')

Manual loop. PuffeRL(config, vecenv, policy, logger=None) — note the first arg is a config dict (not flat kwargs), the env arg is vecenv, and the loop is driven by global_step. The three loop methods are real: evaluate(), train(), mean_and_log().

import pufferlib.vector
from pufferlib.pufferl import PuffeRL, load_config

# Native PufferEnv -> default backend=PufferEnv. For wrapped (Gymnasium/
# PettingZoo) envs you MUST pass backend=pufferlib.vector.Multiprocessing.
vecenv = pufferlib.vector.make(MyPufferEnv, num_envs=256)

# load_config returns a nested args dict (sections: 'train', 'vec', 'env', ...)
# with defaults from pufferlib/config/*.ini. PuffeRL takes the 'train' section.
args = load_config('puffer_breakout')
config = {**args['train'], 'env': 'puffer_breakout'}
config['device'] = 'cuda'

trainer = PuffeRL(config, vecenv, my_policy)
while trainer.global_step < config['total_timesteps']:
    trainer.evaluate()      # Collect rollouts
    trainer.train()         # Train on batch
    trainer.mean_and_log()  # Aggregate + log

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
import gymnasium
from pufferlib import PufferEnv

class MyEnvironment(PufferEnv):
    def __init__(self, buf=None):
        # Define spaces BEFORE calling super().__init__(buf)
        self.single_observation_space = gymnasium.spaces.Box(
            low=-np.inf, high=np.inf, shape=(4,), dtype=np.float32)
        self.single_action_space = gymnasium.spaces.Discrete(4)
        self.num_agents = 1

        super().__init__(buf)

    def reset(self, seed=None):
        # Reset state and return (observation, info-list)
        obs = self._get_observation()
        return obs, []

    def step(self, action):
        # Execute action, compute reward, check termination/truncation
        obs = self._get_observation()
        rewards = self._compute_reward()
        terminals = self._is_done()
        truncations = self._is_truncated()
        info = []

        return obs, rewards, terminals, truncations, 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.vector

# Pass an env-constructor callable. Default backend=PufferEnv is native-only;
# for wrapped (Gymnasium/PettingZoo) envs add backend=pufferlib.vector.Multiprocessing.
env = pufferlib.vector.make(env_creator, num_envs=256, num_workers=8)

# Performance benchmarks (PufferLib's published figures; vary by env/hardware):
# - 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.emulation
import pufferlib.vector

# Wrap a Gymnasium env in a GymnasiumPufferEnv, then vectorize.
# Wrapped (non-native) envs require an explicit backend (Serial or Multiprocessing);
# the default backend=PufferEnv is only for native PufferEnvs.
def env_creator():
    return pufferlib.emulation.GymnasiumPufferEnv(
        env_creator=lambda: gym.make('CartPole-v1'))

env = pufferlib.vector.make(
    env_creator, num_envs=256, backend=pufferlib.vector.Multiprocessing)

PettingZoo multi-agent:

import pufferlib.emulation
import pufferlib.vector
from pettingzoo.butterfly import knights_archers_zombies_v10

# Wrap a PettingZoo env in a PettingZooPufferEnv, then vectorize.
def env_creator():
    return pufferlib.emulation.PettingZooPufferEnv(
        env_creator=lambda: knights_archers_zombies_v10.parallel_env())

env = pufferlib.vector.make(
    env_creator, num_envs=128, backend=pufferlib.vector.Multiprocessing)

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. Wrap with pufferlib.emulation.GymnasiumPufferEnv and vectorize with pufferlib.vector.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

import pufferlib.vector

# Atari (pass an env-constructor callable)
env = pufferlib.vector.make(make_pong_env, num_envs=256)

# Procgen
env = pufferlib.vector.make(make_coinrun_env, num_envs=256)

# Minigrid
env = pufferlib.vector.make(make_minigrid_env, num_envs=256)

Multi-Agent Learning

import pufferlib.vector

# PettingZoo, wrapped via PettingZooPufferEnv (needs an explicit backend)
env = pufferlib.vector.make(
    make_pistonball_env, num_envs=128, backend=pufferlib.vector.Multiprocessing)

# One shared policy serves all agents (single_observation_space / single_action_space
# are per-agent). Pass config (dict), vecenv, policy positionally to PuffeRL.
policy = create_policy(env.single_observation_space, env.single_action_space)
trainer = PuffeRL(config, env, policy)

Custom Task Development

import pufferlib.vector

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

# Native PufferEnv -> default backend=PufferEnv is fine here.
env = pufferlib.vector.make(MyTask, num_envs=256)
trainer = PuffeRL(config, env, my_policy)  # config is a dict (see Training above)

High-Performance Optimization

import pufferlib.vector

# Maximize throughput (pass an env-constructor callable)
env = pufferlib.vector.make(
    my_env_creator,     # env constructor callable
    num_envs=1024,      # Large batch
    num_workers=16,     # Many workers
    backend=pufferlib.vector.Multiprocessing,
)

Installation

# Pin the 3.0 line — the config-dict trainer API and import paths in this skill
# target it. The dev 4.0 branch differs.
uv pip install "pufferlib==3.0.*"

Documentation

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