mlsanityData to 7 Oct 2026

Deep Reinforcement Learning: An Overview

arXiv 1701.07274 ↗First posted 25 Jan 2017cs.LGcited by 327

Not on the map. Why?

Abstract (arXiv)

We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems, machine translation, and text generation, computer vision, neural architecture design, business management, finance, healthcare, Industry 4.0, smart grid, intelligent transportation systems, and computer systems. We mention topics not reviewed yet, and list a collection of RL resources. After presenting a brief summary, we close with discussions. Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant update.

Key facts. Cited by 327 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 51; 2022: 48; 2023: 66; 2024: 63; 2025: 73; 2026: 25.

327 papers.

Sort:

By area:

  1. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  2. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  3. 2023FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Promptscited by 368
  4. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  5. 2023Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimizationcited by 223
  6. 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
  7. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 116
  8. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 110
  9. 2023A Brief Review of Hypernetworks in Deep Learningcited by 102
  10. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  11. 2023Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generationcited by 73
  12. 2025EditScore: Unlocking Online RL for Image Editing via High-Fidelity Reward Modelingcited by 61
38 more
  1. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  2. 2021Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domaincited by 57
  3. 2021Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networkscited by 50
  4. 2021Dynamic Sparse Training for Deep Reinforcement Learningcited by 49
  5. 2023A Survey on Causal Reinforcement Learningcited by 43
  6. 2022Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attackscited by 41
  7. 2024Diffusion Policies creating a Trust Region for Offline Reinforcement Learningcited by 41
  8. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 40
  9. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  10. 2021Deep reinforcement learning in medical imaging: A literature reviewcited by 34
  11. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  12. 2021Towards Scalable Verification of Deep Reinforcement Learningcited by 33
  13. 2022Verifying Learning-Based Robotic Navigation Systemscited by 33
  14. 2022Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capturecited by 31
  15. 2023A Large Language Model Enhanced Conversational Recommender Systemcited by 31
  16. 2023An In-depth Survey of Large Language Model-based Artificial Intelligence Agentscited by 30
  17. 2024Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defensescited by 30
  18. 2021Continuous Doubly Constrained Batch Reinforcement Learningcited by 27
  19. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 27
  20. 2023Effective control of two-dimensional Rayleigh--B'enard convection: invariant multi-agent reinforcement learning is all you needcited by 25
  21. 2025RemoteReasoner: Towards Unifying Geospatial Reasoning Workflowcited by 25
  22. 2021Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Surveycited by 24
  23. 2023Evolutionary Reinforcement Learning: A Surveycited by 24
  24. 2023Formally Explaining Neural Networks within Reactive Systemscited by 24
  25. 2023Neurosymbolic Reinforcement Learning and Planning: A Surveycited by 24
  26. 2021Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learningcited by 22
  27. 2024Prototypical Reward Network for Data-Efficient RLHFcited by 22
  28. 2021Deep Reinforcement Learning for Active High Frequency Tradingcited by 21
  29. 2021Levels of explainable artificial intelligence for human-aligned conversational explanationscited by 21
  30. 2022Intelligent problem-solving as integrated hierarchical reinforcement learningcited by 21
  31. 2024Amortized Bayesian Experimental Design for Decision-Makingcited by 20
  32. 2022Deep Reinforcement Learning for Adaptive Mesh Refinementcited by 19
  33. 2023Recent Developments in Machine Learning Methods for Stochastic Control and Gamescited by 19
  34. 2024Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generationcited by 19
  35. 2025Improving RL Exploration for LLM Reasoning through Retrospective Replaycited by 19
  36. 2022Deep Reinforcement Learning for Inverse Inorganic Materials Designcited by 18
  37. 2021A Survey on Deep Reinforcement Learning for Audio-Based Applicationscited by 17
  38. 2022Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learningcited by 16

50 most cited of 327 All 327 (JSON)