mlsanityData to 7 Oct 2026

A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective

arXiv 2503.23650 ↗First posted 31 Mar 2025cs.LG cs.ROcited by 2

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising approach to addressing motion planning (MoP) challenges in autonomous driving (AD). Despite rapid advancements in RL and AD, a systematic description and interpretation of the RL design process tailored to diverse driving tasks remains underdeveloped. This survey provides a comprehensive review of RL-based MoP for AD, focusing on lessons from task-specific perspectives. We first outline the fundamentals of RL methodologies, and then survey their applications in MoP, analyzing scenario-specific features and task requirements to shed light on their influence on RL design choices. Building on this analysis, we summarize key design experiences, extract insights from various driving task applications, and provide guidance for future implementations. Additionally, we examine the frontier challenges in RL-based MoP, review recent efforts to addresse these challenges, and propose strategies for overcoming unresolved issues.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 115 of its 255 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

115 of 255 references.

Closest ancestors · 20

Same direction, newest first

  1. 2025Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving14 Jan
  2. 2024Knowledge Transfer from Simple to Complex: A Safe and Efficient Reinforcement Learning Framework for Autonomous Driving Decision-Making18 Oct
  3. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey19 Aug
  4. 2024RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes7 May
  5. 2023Parameterized Decision-making with Multi-modal Perception for Autonomous Driving19 Dec
  6. 2023Pay Attention to How You Drive: Safe and Adaptive Model-Based Reinforcement Learning for Off-Road Driving12 Oct
  7. 2023Uncertainty-Aware Decision Transformer for Stochastic Driving Environments28 Sep
  8. 2023Integrating Expert Guidance for Efficient Learning of Safe Overtaking in Autonomous Driving Using Deep Reinforcement Learning18 Aug
  9. 2023Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning28 Jun
  10. 2023Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving5 Jun
  11. 2022Robust AI Driving Strategy for Autonomous Vehicles16 Jul
  12. 2021Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving26 Sep
8 more
  1. 2021Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving21 May
  2. 2021Reward (Mis)design for Autonomous Driving28 Apr
  3. 2021Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learning26 Mar
  4. 2021Hierarchical Program-Triggered Reinforcement Learning Agents For Automated Driving25 Mar
  5. 2021Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving19 Mar
  6. 2021Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving18 Feb
  7. 2021A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning17 Jan
  8. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning6 Jan

Other references · 95

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  4. 2015Continuous control with deep reinforcement learningcited by 3,279
  5. 2023A Survey of Large Language Modelscited by 2,537
  6. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
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  8. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  9. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  10. 2022Planning-oriented Autonomous Drivingcited by 1,068
  11. 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 833
  12. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
38 more
  1. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  2. 2020MOPO: Model-based Offline Policy Optimizationcited by 630
  3. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582
  4. 2021COMBO: Conservative Offline Model-Based Policy Optimizationcited by 365
  5. 2019Control Barrier Functions: Theory and Applicationscited by 332
  6. 2023UniSim: A Neural Closed-Loop Sensor Simulatorcited by 294
  7. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 288
  8. 2018Learning to Drive in a Daycited by 266
  9. 2023DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Modelscited by 204
  10. 2020Reward Machines: Exploiting Reward Function Structure in Reinforcement Learningcited by 183
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  12. 2024A Minimaximalist Approach to Reinforcement Learning from Human Feedbackcited by 141
  13. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarioscited by 124
  14. 2020A Survey of End-to-End Driving: Architectures and Training Methodscited by 123
  15. 2024Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methodscited by 114
  16. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  17. 2022Jump-Start Reinforcement Learningcited by 102
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  20. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Drivingcited by 96
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  26. 2018Safe Reinforcement Learning with Model Uncertainty Estimatescited by 70
  27. 2021Accelerating Robotic Reinforcement Learning via Parameterized Action Primitivescited by 63
  28. 2021Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domaincited by 57
  29. 2019Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Drivingcited by 56
  30. 2021Imitation Learning: Progress, Taxonomies and Challengescited by 56
  31. 2018Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Spacecited by 54
  32. 2022Model-Agnostic Multi-Agent Perception Frameworkcited by 51
  33. 2020Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Surveycited by 48
  34. 2023State-wise Safe Reinforcement Learning: A Surveycited by 48
  35. 2022Causal Inference in Recommender Systems: A Survey and Future Directionscited by 44
  36. 2021Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learningcited by 41
  37. 2024Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuningcited by 41
  38. 2019Social Attention for Autonomous Decision-Making in Dense Trafficcited by 39

50 most cited of 95 All 95 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Vision-Language-Action Models for Autonomous Driving: Past, Present, and Futurecited by 32
  2. 2026Beyond Self-Play: Hierarchical Reasoning for Continuous Motion in Closed-Loop Traffic Simulationcited by 1

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