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rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

arXiv 1909.01500 ↗First posted 3 Sep 2019cs.LG cs.AIcited by 38

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Abstract (arXiv)

Since the recent advent of deep reinforcement learning for game play and simulated robotic control, a multitude of new algorithms have flourished. Most are model-free algorithms which can be categorized into three families: deep Q-learning, policy gradients, and Q-value policy gradients. These have developed along separate lines of research, such that few, if any, code bases incorporate all three kinds. Yet these algorithms share a great depth of common deep reinforcement learning machinery. We are pleased to share rlpyt, which implements all three algorithm families on top of a shared, optimized infrastructure, in a single repository. It contains modular implementations of many common deep RL algorithms in Python using PyTorch, a leading deep learning library. rlpyt is designed as a high-throughput code base for small- to medium-scale research in deep RL. This white paper summarizes its features, algorithms implemented, and relation to prior work, and concludes with detailed implementation and usage notes. rlpyt is available at https://github.com/astooke/rlpyt.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2019.

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Citing papers per year: 2020: 2; 2021: 15; 2022: 9; 2023: 8; 2024: 3; 2025: 1.

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  1. 2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 501
  2. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 139
  3. 2022CORL: Research-oriented Deep Offline Reinforcement Learning Librarycited by 117
  4. 2021Pretraining Representations for Data-Efficient Reinforcement Learningcited by 105
  5. 2023TorchRL: A data-driven decision-making library for PyTorchcited by 59
  6. 2023Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manualscited by 45
  7. 2022skrl: Modular and Flexible Library for Reinforcement Learningcited by 35
  8. 2021Multi-Agent MDP Homomorphic Networkscited by 24
  9. 2021On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learningcited by 23
  10. 2021Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learningcited by 23
  11. 2020Inverse Reinforcement Learning with Natural Language Goalscited by 20
  12. 2021Megaverse: Simulating Embodied Agents at One Million Experiences per Secondcited by 19
26 more
  1. 2022Compositional Multi-Object Reinforcement Learning with Linear Relation Networkscited by 19
  2. 2021Large Batch Simulation for Deep Reinforcement Learningcited by 18
  3. 2021Average-Reward Reinforcement Learning with Trust Region Methodscited by 18
  4. 2022Explain My Surprise: Learning Efficient Long-Term Memory by Predicting Uncertain Outcomescited by 11
  5. 2023SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Corescited by 10
  6. 2020Regularized Inverse Reinforcement Learningcited by 9
  7. 2021ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learningcited by 9
  8. 2022Consistent Dropout for Policy Gradient Reinforcement Learningcited by 9
  9. 2024Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Surveycited by 8
  10. 2021Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discoverycited by 5
  11. 2021Dr Jekyll and Mr Hyde: the Strange Case of Off-Policy Policy Updatescited by 5
  12. 2022RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RLcited by 5
  13. 2022Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learningcited by 5
  14. 2023GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Modelscited by 5
  15. 2021Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precisioncited by 3
  16. 2021Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operationscited by 3
  17. 2023RLLTE: Long-Term Evolution Project of Reinforcement Learningcited by 3
  18. 2024Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement Learningcited by 2
  19. 2022Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task Environmentscited by 1
  20. 2023Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learningcited by 1
  21. 2023Intrinsic Rewards from Self-Organizing Feature Maps for Exploration in Reinforcement Learningcited by 1
  22. 2023Spreeze: High-Throughput Parallel Reinforcement Learning Frameworkcited by 1
  23. 2024Towards Fault Tolerance in Multi-Agent Reinforcement Learningcited by 1
  24. 2025Tempo: Compiled Dynamic Deep Learning with Symbolic Dependence Graphscited by 1
  25. 2021The AI Arena: A Framework for Distributed Multi-Agent Reinforcement Learningcited by 0
  26. 2022Using Forwards-Backwards Models to Approximate MDP Homomorphismscited by 0