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An Introduction to Deep Reinforcement Learning

arXiv 1811.12560 ↗First posted 30 Nov 2018cs.LG cs.AI stat.MLcited by 206

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Cited by 57 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.

Key facts. Cited by 206 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2018.

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Citing papers per year: 2021: 41; 2022: 35; 2023: 31; 2024: 41; 2025: 40; 2026: 18.

206 papers.

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  1. 2021Recent Advances in Reinforcement Learning in Financecited by 110
  2. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  3. 2021Learning Successor States and Goal-Dependent Values: A Mathematical Viewpointcited by 70
  4. 2024Large Language Models for Cyber Security: A Systematic Literature Reviewcited by 68
  5. 2023Prompt Engineering for Healthcare: Methodologies and Applicationscited by 52
  6. 2021Deep Reinforcement Learning in Computer Vision: A Comprehensive Surveycited by 43
  7. 2021How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Reviewcited by 39
  8. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  9. 2021Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limitscited by 38
  10. 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
  11. 2025MDPO: Overcoming the Training-Inference Divide of Masked Diffusion Language Modelscited by 32
  12. 2023Sample Complexity of Variance-reduced Distributionally Robust Q-learningcited by 29
38 more
  1. 2021Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Surveycited by 28
  2. 2022An information-theoretic perspective on intrinsic motivation in reinforcement learning: a surveycited by 28
  3. 2024A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Modelcited by 27
  4. 2022A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learningcited by 25
  5. 2021Reinforcement Learning for IoT Security: A Comprehensive Surveycited by 23
  6. 2022Multi-Stage Episodic Control for Strategic Exploration in Text Gamescited by 22
  7. 2022Learning Generalized Policies Without Supervision Using GNNscited by 22
  8. 2021Reinforcement Learning for Feedback-Enabled Cyber Resiliencecited by 19
  9. 2021Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approachcited by 18
  10. 2021Deep Learning for Android Malware Defenses: a Systematic Literature Reviewcited by 18
  11. 2024A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Drivingcited by 18
  12. 2021A Survey on Deep Reinforcement Learning for Audio-Based Applicationscited by 17
  13. 2024PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preferencecited by 17
  14. 2022Distributed Reinforcement Learning for Robot Teams: A Reviewcited by 16
  15. 2021A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertisingcited by 15
  16. 2024The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Modelscited by 15
  17. 2024Graph Neural Networks for Job Shop Scheduling Problems: A Surveycited by 15
  18. 2023Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learningcited by 12
  19. 2022MetaAID: A Flexible Framework for Developing Metaverse Applications via AI Technology and Human Editingcited by 11
  20. 2022Towards out of distribution generalization for problems in mechanicscited by 11
  21. 2022A Meta-Reinforcement Learning Algorithm for Causal Discoverycited by 11
  22. 2023Referential communication in heterogeneous communities of pre-trained visual deep networkscited by 10
  23. 2023Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RLcited by 10
  24. 2023Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimationcited by 10
  25. 2024Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learningcited by 10
  26. 2025From Screens to Scenes: A Survey of Embodied AI in Healthcarecited by 10
  27. 2025Benchmarking Quantum Reinforcement Learningcited by 10
  28. 2021Linear Representation Meta-Reinforcement Learning for Instant Adaptationcited by 9
  29. 2021Generalization in Deep RL for TSP Problems via Equivariance and Local Searchcited by 9
  30. 2021Full Gradient DQN Reinforcement Learning: A Provably Convergent Schemecited by 8
  31. 2021Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learningcited by 8
  32. 2021Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detectioncited by 8
  33. 2022AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trendscited by 8
  34. 2023Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunitiescited by 8
  35. 2024FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficientlycited by 8
  36. 2024Generating and Evolving Reward Functions for Highway Driving with Large Language Modelscited by 8
  37. 2025The impact of intrinsic rewards on exploration in Reinforcement Learningcited by 8
  38. 2022Understanding the Evolution of Linear Regions in Deep Reinforcement Learningcited by 7

50 most cited of 206 All 206 (JSON)