mlsanityData to 7 Oct 2026

Deep Reinforcement Learning for Autonomous Driving: A Survey

arXiv 2002.00444 ↗First posted 2 Feb 2020cs.LG cs.AI cs.ROcited by 698

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.

Key facts. Cited by 698 papers in the map (data through 7 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. First posted on arXiv 2 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 67; 2022: 99; 2023: 131; 2024: 167; 2025: 163; 2026: 70.

698 papers.

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  1. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  2. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 364
  3. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 288
  4. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 228
  5. 2023LanguageMPC: Large Language Models as Decision Makers for Autonomous Drivingcited by 188
  6. 2021BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environmentscited by 161
  7. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
  8. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  9. 2024Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospectscited by 130
  10. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradientscited by 122
  11. 2021Reward (Mis)design for Autonomous Drivingcited by 102
  12. 2025A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Modelscited by 87
38 more
  1. 2023Diffusion Models for Reinforcement Learning: A Surveycited by 84
  2. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  3. 2021Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithmscited by 76
  4. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  5. 2022SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehiclescited by 65
  6. 2024Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Modelcited by 62
  7. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
  8. 2023CAT: Closed-loop Adversarial Training for Safe End-to-End Drivingcited by 52
  9. 2021Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Drivingcited by 46
  10. 2021Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approachcited by 46
  11. 2023Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manualscited by 45
  12. 2022Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Modelcited by 44
  13. 2022Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guaranteescited by 42
  14. 2021Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systemscited by 36
  15. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 36
  16. 2025Multi-agent Embodied AI: Advances and Future Directionscited by 36
  17. 2022On Transforming Reinforcement Learning by Transformer: The Development Trajectorycited by 35
  18. 2023Towards Unified Deep Image Deraining: A Survey and A New Benchmarkcited by 34
  19. 2020Robust Inverse Reinforcement Learning under Transition Dynamics Mismatchcited by 33
  20. 2025Vision-Language-Action Models for Autonomous Driving: Past, Present, and Futurecited by 32
  21. 2022Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarkingcited by 31
  22. 2024HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Drivingcited by 31
  23. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 30
  24. 2021PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulationcited by 29
  25. 2025Maximum Entropy Reinforcement Learning with Diffusion Policycited by 29
  26. 2021Learning Graphon Mean Field Games and Approximate Nash Equilibriacited by 28
  27. 2023PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learningcited by 28
  28. 2023Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorialcited by 27
  29. 2022Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learningcited by 26
  30. 2023When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learningcited by 26
  31. 2021Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligencecited by 25
  32. 2023The Generalization Gap in Offline Reinforcement Learningcited by 25
  33. 2021Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Valuescited by 24
  34. 2022Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Drivingcited by 24
  35. 2023ICICLE: Interpretable Class Incremental Continual Learningcited by 24
  36. 2024Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithmscited by 24
  37. 2024Cross-Domain Policy Adaptation by Capturing Representation Mismatchcited by 24
  38. 2025Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Drivingcited by 24

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  1. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  2. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  3. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Driving
  4. 2024Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study
  5. 2022Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning
  6. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving
  7. 2024Self-Driving Car Racing: Application of Deep Reinforcement Learning
  8. 2020Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning