mlsanityData to 7 Oct 2026

Deep Reinforcement Learning framework for Autonomous Driving

arXiv 1704.02532 ↗First posted 8 Apr 2017stat.ML cs.LG cs.ROcited by 224

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Cited by 37 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning. This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks. As it is a relatively new area of research for autonomous driving, we provide a short overview of deep reinforcement learning and then describe our proposed framework. It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios. It also integrates the recent work on attention models to focus on relevant information, thereby reducing the computational complexity for deployment on embedded hardware. The framework was tested in an open source 3D car racing simulator called TORCS. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction of other vehicles.

Key facts. Cited by 224 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2018: 1; 2019: 1; 2020: 2; 2021: 69; 2022: 47; 2023: 34; 2024: 28; 2025: 27; 2026: 15.

224 papers.

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  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582
  3. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 364
  4. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 169
  5. 2021Robust Reinforcement Learning on State Observations with Learned Optimal Adversarycited by 133
  6. 2022Frequency Domain Model Augmentation for Adversarial Attackcited by 128
  7. 2022A Review of Off-Policy Evaluation in Reinforcement Learningcited by 92
  8. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  9. 2021Off-Policy Imitation Learning from Observationscited by 70
  10. 2021Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximationcited by 69
  11. 2022SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehiclescited by 65
  12. 2023TorchRL: A data-driven decision-making library for PyTorchcited by 59
38 more
  1. 2021A Survey of Machine Learning for Computer Architecture and Systemscited by 53
  2. 2021Decentralized Q-Learning in Zero-sum Markov Gamescited by 53
  3. 2022Safe Reinforcement Learning via Shielding under Partial Observabilitycited by 49
  4. 2021Triangle Attack: A Query-efficient Decision-based Adversarial Attackcited by 48
  5. 2021Offline Reinforcement Learning as Anti-Explorationcited by 44
  6. 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
  7. 2021CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothingcited by 41
  8. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 40
  9. 2021Deeply-Debiased Off-Policy Interval Estimationcited by 36
  10. 2022DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehiclescited by 36
  11. 2023Policy Optimization for Continuous Reinforcement Learningcited by 30
  12. 2021Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancementcited by 28
  13. 2021FedAdapt: Adaptive Offloading for IoT Devices in Federated Learningcited by 26
  14. 2021Online Bootstrap Inference For Policy Evaluation in Reinforcement Learningcited by 26
  15. 2022Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learningcited by 26
  16. 2021Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligencecited by 25
  17. 2021Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learningcited by 25
  18. 2022COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attackscited by 25
  19. 2022BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasetscited by 24
  20. 2022What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?cited by 21
  21. 2022Offline Reinforcement Learning with Differential Privacycited by 19
  22. 2023Reward Shaping for Happier Autonomous Cyber Security Agentscited by 19
  23. 2021Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approachcited by 18
  24. 2021A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learningcited by 18
  25. 2021Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution Systemscited by 18
  26. 2021Black-Box Testing of Deep Neural Networks Through Test Case Diversitycited by 18
  27. 2021A Survey on Deep Reinforcement Learning for Audio-Based Applicationscited by 17
  28. 2021Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operatorscited by 17
  29. 2023Inroads into Autonomous Network Defence using Explained Reinforcement Learningcited by 17
  30. 2024LangProp: A code optimization framework using Large Language Models applied to drivingcited by 17
  31. 2021Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Drivingcited by 16
  32. 2021Optimistic Policy Optimization is Provably Efficient in Non-stationary MDPscited by 16
  33. 2022CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Drivingcited by 16
  34. 2022Near-Optimal Differentially Private Reinforcement Learningcited by 16
  35. 2023SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learningcited by 16
  36. 2022Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersectionscited by 15
  37. 2022Reinforcement Learning in Medical Image Analysis: Concepts, Applications, Challenges, and Future Directionscited by 15
  38. 2023Online Learning in Stackelberg Games with an Omniscient Followercited by 15

50 most cited of 224 All 224 (JSON)