Stable-Baselines3 리뷰

RLlib excels in scalability for complex or distributed reinforcement learning workloads, supporting multi-agent setups and large-scale parallel training across clusters.

While Stable-Baselines3 focuses on reliable, user-friendly implementations for single-machine training, RLlib is designed for production-level, highly scalable, and fault-tolerant RL workloads across distributed computing environments. It integrates with both TensorFlow and PyTorch, offering broader backend compatibility than Stable-Baselines3's PyTorch-only foundation.

TF-Agents is an open-source library from Google for building reinforcement learning algorithms and environments using the TensorFlow ecosystem, providing a modular design for customizing components.

TF-Agents is built on TensorFlow, whereas Stable-Baselines3 is built on PyTorch. Both provide implementations of various RL algorithms, but TF-Agents leverages TensorFlow's powerful capabilities and is ideal for those already working within the TensorFlow framework.

Keras-RL2 provides a simple and easy-to-use library for implementing reinforcement learning algorithms in Keras, making it particularly beginner-friendly.

Keras-RL2 offers a simpler API for beginners, similar to Stable-Baselines3's user-friendliness, but it is built on Keras (which can use TensorFlow as a backend), contrasting with Stable-Baselines3's PyTorch foundation.

Tianshou is a flexible and customizable PyTorch-based library designed for reinforcement learning research, offering a clean and modular API for implementing various RL algorithms.

Both Tianshou and Stable-Baselines3 are PyTorch-based and provide implementations of RL algorithms. Tianshou emphasizes flexibility and customizability for research, potentially offering more granular control for advanced users compared to Stable-Baselines3's focus on reliable, out-of-the-box implementations.