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Dopamine: A Research Framework for Deep Reinforcement Learning

arXiv 1812.06110 ↗First posted 14 Dec 2018cs.LG cs.AIcited by 121

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Abstract (arXiv)

Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provide stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become more diverse in its goals. In this paper we introduce Dopamine, a new research framework for deep RL that aims to support some of that diversity. Dopamine is open-source, TensorFlow-based, and provides compact and reliable implementations of some state-of-the-art deep RL agents. We complement this offering with a taxonomy of the different research objectives in deep RL research. While by no means exhaustive, our analysis highlights the heterogeneity of research in the field, and the value of frameworks such as ours.

Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Dec 2018.

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Citing papers per year: 2019: 2; 2020: 1; 2021: 37; 2022: 22; 2023: 21; 2024: 21; 2025: 13; 2026: 4.

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  1. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 781
  2. 2022Multi-Game Decision Transformerscited by 212
  3. 2021Benchmarking the Spectrum of Agent Capabilitiescited by 193
  4. 2023The Dormant Neuron Phenomenon in Deep Reinforcement Learningcited by 171
  5. 2023Bigger, Better, Faster: Human-level Atari with human-level efficiencycited by 145
  6. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 139
  7. 2024Stop Regressing: Training Value Functions via Classification for Scalable Deep RLcited by 136
  8. 2021Avalanche: an End-to-End Library for Continual Learningcited by 124
  9. 2022CORL: Research-oriented Deep Offline Reinforcement Learning Librarycited by 117
  10. 2019Benchmarking Batch Deep Reinforcement Learning Algorithmscited by 108
  11. 2021Pretraining Representations for Data-Efficient Reinforcement Learningcited by 105
  12. 2021Generalized Decision Transformer for Offline Hindsight Information Matchingcited by 105
38 more
  1. 2021d3rlpy: An Offline Deep Reinforcement Learning Librarycited by 84
  2. 2024Mixtures of Experts Unlock Parameter Scaling for Deep RLcited by 75
  3. 2022Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizescited by 72
  4. 2024Simplifying Deep Temporal Difference Learningcited by 71
  5. 2021Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspectivecited by 64
  6. 2022Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progresscited by 63
  7. 2022The State of Sparse Training in Deep Reinforcement Learningcited by 60
  8. 2023TorchRL: A data-driven decision-making library for PyTorchcited by 59
  9. 2020RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learningcited by 57
  10. 2021FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Financecited by 57
  11. 2021The Difficulty of Passive Learning in Deep Reinforcement Learningcited by 50
  12. 2021MICo: Improved representations via sampling-based state similarity for Markov decision processescited by 43
  13. 2024From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessonscited by 42
  14. 2025Towards General-Purpose Model-Free Reinforcement Learningcited by 40
  15. 2022Mask-based Latent Reconstruction for Reinforcement Learningcited by 39
  16. 2024In value-based deep reinforcement learning, a pruned network is a good networkcited by 38
  17. 2019An Optimistic Perspective on Offline Reinforcement Learningcited by 37
  18. 2021On Bonus-Based Exploration Methods in the Arcade Learning Environmentcited by 37
  19. 2023Small batch deep reinforcement learningcited by 35
  20. 2024Gymnasium: A Standard Interface for Reinforcement Learning Environmentscited by 35
  21. 2021Provable Representation Learning for Imitation with Contrastive Fourier Featurescited by 34
  22. 2021Learning Long-Term Reward Redistribution via Randomized Return Decompositioncited by 33
  23. 2022On the Generalization of Representations in Reinforcement Learningcited by 28
  24. 2023Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learningcited by 28
  25. 2023Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stagescited by 27
  26. 2023Resetting the Optimizer in Deep RL: An Empirical Studycited by 26
  27. 2022Hyperbolic Deep Reinforcement Learningcited by 25
  28. 2021RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystemscited by 21
  29. 2021Distributional Reinforcement Learning for Multi-Dimensional Reward Functionscited by 21
  30. 2024On the consistency of hyper-parameter selection in value-based deep reinforcement learningcited by 21
  31. 2021A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representationcited by 19
  32. 2021Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approachcited by 18
  33. 2024Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learningcited by 17
  34. 2021Object-Aware Regularization for Addressing Causal Confusion in Imitation Learningcited by 16
  35. 2021Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learningcited by 15
  36. 2021Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillationcited by 15
  37. 2021Large Batch Experience Replaycited by 14
  38. 2024Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RLcited by 14

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