cleanrl
A deep reinforcement learning library offering high-quality, single-file implementations of algorithms like PPO, DQN, and SAC for research and education.
What is cleanrl?
CleanRL is a Deep Reinforcement Learning library that provides high-quality, single-file implementations of popular algorithms like PPO, DQN, and SAC. It is designed to be a clear, readable reference for researchers and learners who want to understand the complete implementation details of DRL algorithms without the abstraction layers of larger modular libraries. The project solves the problem of opaque or overly complex codebases by offering minimal, standalone scripts that are easy to study, modify, and extend for advanced research prototypes.
Reinforcement learning researchers, students, and practitioners who need to deeply understand algorithm implementations, prototype new features not supported by modular libraries, or seek a transparent and benchmarked codebase for experiments.
Developers choose CleanRL for its unparalleled code clarity and simplicity, which facilitates easier debugging, learning, and prototyping compared to more abstract modular libraries. Its single-file approach, comprehensive benchmarking, and research-oriented features like integrated experiment tracking provide a unique blend of educational value and practical utility for advanced DRL projects.
Overview
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Use Cases
Best For
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