When should I choose PufferLib over Stable-Baselines3?

Use PufferLib when your priority is speed, scale, or multi-agent support. Choose Stable-Baselines3 for quick prototyping of standard algorithms where throughput is not the primary bottleneck.

What is the PufferLib Ocean suite?

The Ocean suite is a collection of over 20 pre-built, high-performance environments included with PufferLib, designed for immediate training and benchmarking.

What makes PufferLib faster than other RL libraries?

PufferLib uses optimized vectorization, shared memory buffers, and a high-performance PPO+LSTM implementation (PuffeRL) to achieve 2-10x speedups over standard implementations like Stable-Baselines3.

Can I use my existing Gymnasium environments with PufferLib?

Absolutely. PufferLib includes emulation wrappers that allow you to seamlessly vectorize and accelerate standard Gymnasium environments for much faster training.

Does this skill support multi-agent reinforcement learning?

Yes, PufferLib provides native support for multi-agent systems and integrates directly with PettingZoo, allowing for high-performance training of cooperative or competitive agents.

PufferLib Reinforcement Learning

Name: PufferLib Reinforcement Learning
Author: Sologa

bySologa

데이터 과학 및 ML

Accelerates reinforcement learning workflows with high-performance environment vectorization and optimized PPO training.

소개

PufferLib is a specialized skill for Claude Code designed to streamline the development and training of high-performance reinforcement learning agents. It provides optimized implementations of Proximal Policy Optimization (PPO) and LSTM architectures, achieving training speeds of millions of steps per second through advanced environment vectorization. Whether you are building custom environments with the PufferEnv API or integrating with standard frameworks like Gymnasium and PettingZoo, this skill offers the implementation patterns and architectural guidance needed to scale RL experimentation and achieve 2-10x speedups over standard implementations.

주요 기능

High-speed PPO+LSTM training (PuffeRL) achieving millions of steps per second
Native support for complex multi-agent reinforcement learning (MARL) systems
0 GitHub stars
Advanced environment vectorization using shared memory for zero-copy performance
Comprehensive templates for custom environment creation and performance profiling
Seamless integration with Gymnasium, PettingZoo, Atari, and Procgen frameworks

사용 사례

Training complex RL agents on vectorized game environments like Atari, Procgen, or NetHack
Optimizing existing RL pipelines to increase simulation throughput and reduce training time
Developing custom high-performance multi-agent environments using the PufferEnv API

소개

주요 기능

High-speed PPO+LSTM training (PuffeRL) achieving millions of steps per second
Native support for complex multi-agent reinforcement learning (MARL) systems
0 GitHub stars
Advanced environment vectorization using shared memory for zero-copy performance
Comprehensive templates for custom environment creation and performance profiling
Seamless integration with Gymnasium, PettingZoo, Atari, and Procgen frameworks

사용 사례

Training complex RL agents on vectorized game environments like Atari, Procgen, or NetHack
Optimizing existing RL pipelines to increase simulation throughput and reduce training time
Developing custom high-performance multi-agent environments using the PufferEnv API