mlsanityData to 7 Oct 2026

Exploration by Random Network Distillation

arXiv 1810.12894 ↗First posted 30 Oct 2018cs.LG cs.AI stat.MLcited by 966

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Cited by 76 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce an exploration bonus for deep reinforcement learning methods that is easy to implement and adds minimal overhead to the computation performed. The bonus is the error of a neural network predicting features of the observations given by a fixed randomly initialized neural network. We also introduce a method to flexibly combine intrinsic and extrinsic rewards. We find that the random network distillation (RND) bonus combined with this increased flexibility enables significant progress on several hard exploration Atari games. In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods. To the best of our knowledge, this is the first method that achieves better than average human performance on this game without using demonstrations or having access to the underlying state of the game, and occasionally completes the first level.

Key facts. Cited by 966 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Oct 2018.

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Citing papers per year: 2019: 3; 2020: 9; 2021: 130; 2022: 149; 2023: 173; 2024: 150; 2025: 193; 2026: 159.

966 papers.

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  1. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  2. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582
  3. 2022Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videoscited by 338
  4. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 298
  5. 2025Reasoning with Exploration: An Entropy Perspectivecited by 277
  6. 2021A Survey of Embodied AI: From Simulators to Research Taskscited by 256
  7. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
  8. 2022CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigationcited by 194
  9. 2021Benchmarking the Spectrum of Agent Capabilitiescited by 193
  10. 2024DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Searchcited by 189
  11. 2023Guiding Pretraining in Reinforcement Learning with Large Language Modelscited by 185
  12. 2021Reinforcement Learning with Prototypical Representationscited by 179
38 more
  1. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
  2. 2022Hybrid RL: Using Both Offline and Online Data Can Make RL Efficientcited by 158
  3. 2021URLB: Unsupervised Reinforcement Learning Benchmarkcited by 138
  4. 2021Discovering and Achieving Goals via World Modelscited by 131
  5. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  6. 2020Primal Wasserstein Imitation Learningcited by 120
  7. 2021Rapid Exploration for Open-World Navigation with Latent Goal Modelscited by 118
  8. 2023Robot Parkour Learningcited by 115
  9. 2019Benchmarking Batch Deep Reinforcement Learning Algorithmscited by 108
  10. 2021State Entropy Maximization with Random Encoders for Efficient Explorationcited by 107
  11. 2022Reinforcement Learning with Action-Free Pre-Training from Videoscited by 107
  12. 2022Is Power-Seeking AI an Existential Risk?cited by 101
  13. 2023Motif: Intrinsic Motivation from Artificial Intelligence Feedbackcited by 99
  14. 2025Maximizing Confidence Alone Improves Reasoningcited by 97
  15. 2021Celebrating Diversity in Shared Multi-Agent Reinforcement Learningcited by 96
  16. 2021MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Researchcited by 96
  17. 2022Deep Hierarchical Planning from Pixelscited by 96
  18. 2022Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learningcited by 93
  19. 2021Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Interventioncited by 90
  20. 2024Foundation Policies with Hilbert Representationscited by 89
  21. 2024Curiosity-driven Red-teaming for Large Language Modelscited by 83
  22. 2024Is Value Learning Really the Main Bottleneck in Offline RL?cited by 82
  23. 2023METRA: Scalable Unsupervised RL with Metric-Aware Abstractioncited by 80
  24. 2024Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learningcited by 80
  25. 2024Open-Endedness is Essential for Artificial Superhuman Intelligencecited by 79
  26. 2025Outcome-based Exploration for LLM Reasoningcited by 79
  27. 2025AgentEvolver: Towards Efficient Self-Evolving Agent Systemcited by 77
  28. 2023Video Prediction Models as Rewards for Reinforcement Learningcited by 76
  29. 2021DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representationscited by 73
  30. 2021Asymmetric self-play for automatic goal discovery in robotic manipulationcited by 71
  31. 2022BYOL-Explore: Exploration by Bootstrapped Predictioncited by 71
  32. 2021Deep Graph-level Anomaly Detection by Glocal Knowledge Distillationcited by 67
  33. 2022CIC: Contrastive Intrinsic Control for Unsupervised Skill Discoverycited by 66
  34. 2022Reward Uncertainty for Exploration in Preference-based Reinforcement Learningcited by 60
  35. 2023Generative Flow Networks as Entropy-Regularized RLcited by 60
  36. 2022Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexitycited by 59
  37. 2022MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrationscited by 59
  38. 2023POPGym: Benchmarking Partially Observable Reinforcement Learningcited by 59

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