mlsanityData to 7 Oct 2026

A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving

arXiv 2110.11573 ↗First posted 22 Oct 2021cs.RO cs.AIcited by 5

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

Abstract (arXiv)

Heated debates continue over the best autonomous driving framework. The classic modular pipeline is widely adopted in the industry owing to its great interpretability and stability, whereas the fully end-to-end paradigm has demonstrated considerable simplicity and learnability along with the rise of deep learning. As a way of marrying the advantages of both approaches, learning a semantically meaningful representation and then use in the downstream driving policy learning tasks provides a viable and attractive solution. However, several key challenges remain to be addressed, including identifying the most effective representation, alleviating the sim-to-real generalization issue as well as balancing model training cost. In this study, we propose a versatile and efficient reinforcement learning framework and build a fully functional autonomous vehicle for real-world validation. Our framework shows great generalizability to various complicated real-world scenarios and superior training efficiency against the competing baselines.

Key facts. Cited by 5 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 45 of its 56 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

45 of 56 references.

References · 45

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  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
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33 more
  1. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  2. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 616
  3. 2018On Evaluation of Embodied Navigation Agentscited by 526
  4. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  5. 2020Reinforcement Learning with Augmented Datacited by 452
  6. 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 373
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  8. 2018Quantifying Generalization in Reinforcement Learningcited by 345
  9. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 343
  10. 2019Causal Confusion in Imitation Learningcited by 286
  11. 2018Learning to Drive in a Daycited by 266
  12. 2018Distributed Prioritized Experience Replaycited by 260
  13. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 229
  14. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  15. 2018State Representation Learning for Control: An Overviewcited by 125
  16. 2020A Survey of End-to-End Driving: Architectures and Training Methodscited by 123
  17. 2019Multimodal End-to-End Autonomous Drivingcited by 110
  18. 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 99
  19. 2018CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-drivingcited by 96
  20. 2018Driving Policy Transfer via Modularity and Abstractioncited by 91
  21. 2020RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Realcited by 89
  22. 2019Urban Driving with Conditional Imitation Learningcited by 84
  23. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  24. 2017Virtual to Real Reinforcement Learning for Autonomous Drivingcited by 79
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  26. 2019Simulation-based reinforcement learning for real-world autonomous drivingcited by 51
  27. 2020Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understandingcited by 42
  28. 2018Learning to Drive from Simulation without Real World Labelscited by 39
  29. 2020Label Efficient Visual Abstractions for Autonomous Drivingcited by 33
  30. 2019Learning a Decision Module by Imitating Driver's Control Behaviorscited by 17
  31. 2020Learning Accurate and Human-Like Driving using Semantic Maps and Attentioncited by 10
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  33. 2019Affordance Learning In Direct Perception for Autonomous Drivingcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 2; 2026: 1.

5 papers.

  1. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  2. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  3. 2025Interpretable Decision-Making for End-to-End Autonomous Drivingcited by 4
  4. 2025Action Space Reduction Strategies for Reinforcement Learning in Autonomous Drivingcited by 1
  5. 2026The Constant Eye: Benchmarking and Bridging Appearance Robustness in Autonomous Drivingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  2. 2025DriveMind: A Dual Visual Language Model-based Reinforcement Learning Framework for Autonomous Driving
  3. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  4. 2025V-Max: A Reinforcement Learning Framework for Autonomous Driving
  5. 2021Towards Learning Generalizable Driving Policies from Restricted Latent Representations
  6. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Driving
  7. 2026MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving
  8. 2024From Imitation to Exploration: End-to-end Autonomous Driving based on World Model