mlsanityData to 7 Oct 2026

Continuous control with deep reinforcement learning

arXiv 1509.02971 ↗First posted 9 Sep 2015cs.LG stat.MLcited by 3,279

Not on the map. Why?

Cited by 300 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.

Key facts. Cited by 3,279 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Sep 2015.

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Citing papers per year: 2018: 11; 2019: 18; 2020: 22; 2021: 674; 2022: 638; 2023: 578; 2024: 534; 2025: 506; 2026: 298.

3,279 papers.

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  1. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  2. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  3. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  4. 2023Consistency Modelscited by 1,778
  5. 2023Mastering Diverse Domains through World Modelscited by 1,193
  6. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 882
  7. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 781
  8. 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 699
  9. 2019Behavior Regularized Offline Reinforcement Learningcited by 654
  10. 2020MOPO: Model-based Offline Policy Optimizationcited by 630
  11. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582
  12. 2022Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learningcited by 543
38 more
  1. 2022Is Conditional Generative Modeling all you need for Decision-Making?cited by 504
  2. 2023TD-MPC2: Scalable, Robust World Models for Continuous Controlcited by 482
  3. 2018Scalable agent alignment via reward modeling: a research directioncited by 438
  4. 2023Directly Fine-Tuning Diffusion Models on Differentiable Rewardscited by 431
  5. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 430
  6. 2022Temporal Difference Learning for Model Predictive Controlcited by 375
  7. 2022DayDreamer: World Models for Physical Robot Learningcited by 372
  8. 2021Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learningcited by 359
  9. 2022Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoningcited by 347
  10. 2022CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learningcited by 342
  11. 2023Efficient Online Reinforcement Learning with Offline Datacited by 332
  12. 2021Randomized Ensembled Double Q-Learning: Learning Fast Without a Modelcited by 278
  13. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  14. 2025Learning to Reason under Off-Policy Guidancecited by 266
  15. 2024Diffusion Policy Policy Optimizationcited by 255
  16. 2023Using Human Feedback to Fine-tune Diffusion Models without Any Reward Modelcited by 246
  17. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 240
  18. 2021Offline Reinforcement Learning with Fisher Divergence Critic Regularizationcited by 237
  19. 2021Mastering Atari Games with Limited Datacited by 235
  20. 2023Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuningcited by 230
  21. 2022Pre-Trained Language Models for Interactive Decision-Makingcited by 220
  22. 2024From Persona to Personalization: A Survey on Role-Playing Language Agentscited by 206
  23. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
  24. 2024NaVILA: Legged Robot Vision-Language-Action Model for Navigationcited by 201
  25. 2022Online Decision Transformercited by 194
  26. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 191
  27. 2021RvS: What is Essential for Offline RL via Supervised Learning?cited by 184
  28. 2021Reinforcement Learning with Prototypical Representationscited by 179
  29. 2022Offline Reinforcement Learning via High-Fidelity Generative Behavior Modelingcited by 177
  30. 2023The Dormant Neuron Phenomenon in Deep Reinforcement Learningcited by 171
  31. 2021Maximum Entropy RL (Provably) Solves Some Robust RL Problemscited by 169
  32. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 169
  33. 2022Compute Trends Across Three Eras of Machine Learningcited by 161
  34. 2023Foundation Models for Decision Making: Problems, Methods, and Opportunitiescited by 160
  35. 2019Goal-conditioned Imitation Learningcited by 159
  36. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
  37. 2022Hybrid RL: Using Both Offline and Online Data Can Make RL Efficientcited by 158
  38. 2022The Unsurprising Effectiveness of Pre-Trained Vision Models for Controlcited by 155

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