mlsanityData to 7 Oct 2026

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

arXiv 2111.08819 ↗First posted 16 Nov 2021cs.LGcited by 3

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Abstract (arXiv)

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

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 17 references that are in the map. First posted on arXiv 16 Nov 2021.

Builds on

13 of 17 references.

References · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  6. 2017A Distributional Perspective on Reinforcement Learningcited by 743
  7. 2019Leveraging Procedural Generation to Benchmark Reinforcement Learningcited by 478
  8. 2018Distributed Prioritized Experience Replaycited by 260
  9. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 160
  10. 2019ChainerRL: A Deep Reinforcement Learning Librarycited by 51
  11. 2020MushroomRL: Simplifying Reinforcement Learning Researchcited by 50
  12. 2020Griddly: A platform for AI research in gamescited by 31
1 more
  1. 2021Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learningcited by 19

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Cited by year

Citing papers per year: 2023: 1; 2024: 2.

3 papers.

  1. 2024ODRL: A Benchmark for Off-Dynamics Reinforcement Learningcited by 25
  2. 2024Differentiable Discrete Event Simulation for Queuing Network Controlcited by 3
  3. 2023Virtual Augmented Reality for Atari Reinforcement Learningcited by 1