CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms

• URL: http://arxiv.org/abs/2111.08819v1
• Date: Tue, 16 Nov 2021 22:44:56 GMT
• Title: CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
• Authors: Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga
• Abstract summary: CleanRL is an open-source library that provides high-quality single-file implementations of Deep Reinforcement Learning algorithms. It provides a simpler yet scalable developing experience by having a straightforward and integrating production tools.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: CleanRL is an open-source library that provides high-quality single-file implementations of Deep Reinforcement Learning algorithms. It provides a simpler yet scalable developing experience by having a straightforward codebase and integrating production tools to help interact and scale experiments. In CleanRL, we put all details of an algorithm into a single file, making these performance-relevant details easier to recognize. Additionally, an experiment tracking feature is available to help log metrics, hyperparameters, videos of an agent's gameplay, dependencies, and more to the cloud. Despite succinct implementations, we have also designed tools to help scale, at one point orchestrating experiments on more than 2000 machines simultaneously via Docker and cloud providers. Finally, we have ensured the quality of the implementations by benchmarking against a variety of environments. The source code of CleanRL can be found at https://github.com/vwxyzjn/cleanrl

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