Deep reinforcement learning for autonomous driving: A survey
Open accessAbstract
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.
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Most RL-based driving studies have focused on safety constraints or reward shaping, without explicitly operationalizing ethical reasoning across multiple road users.
“Still, most RL-based driving studies have focused on safety constraints or reward shaping, without explicitly operationalizing ethical reasoning across multiple road users.”
From Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving (2026) · cites this paper as [12]
The survey’s sections on intrinsic rewards and incorporating safety in DRL discuss reward shaping and safety-focused approaches; the former calls reward shaping the most common solution to reward design. But the survey does not establish that most RL-based driving studies focus on those approaches, or explicitly assess whether they operationalize ethical reasoning across multiple road users. Page numbers were not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRobots still struggle to solve high-level tasks such as locomotion, autonomous driving and dexterous manipulation, even though low-level controllers and motion planning algorithms are robust enough to drive joints and end-effectors to desired configurations.
“Even though low-level controllers and motion planning algorithms are robust enough to drive the joints and end-effectors to reach their desired configuration, robots still struggle to solve high-level tasks, such as locomotion [3], autonomous driving [4] and dexterous manipulation [5].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [4]
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Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,182 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588 · see citing works
Watkins, C., & Dayan, P. (1992) · Machine-mediated learning · doi:10.1023/a:1022676722315 · Cited by 14,277 · see citing works
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782 · see citing works
Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017) · arXiv · doi:10.48550/arxiv.1707.06347 · Cited by 10,625 · see citing works
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084 · see citing works
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512 · see citing works
Domain-Adversarial Training of Neural Networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318 · see citing works
Markov decision processes: Discrete stochastic dynamic programming
Hazeghi, K., & Puterman, M. L. (1995) · Journal of the American Statistical Association · doi:10.2307/2291177 · Cited by 8,037 · see citing works
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 7,936 · see citing works
Watkins, C. (1989) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17863/cam.132164 · Cited by 5,406 · see citing works
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1× · see citing works
“Authors in [63] formalised training strategies in the context of machine learning that show that improvements in generalisation and in speed of convergence of the training.”
BackgroundTrust region policy optimization
Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1502.05477 · Cited by 4,578 · see citing works
The Behavior of Organisms: An Experimental Analysis
Jones, F. N., & Skinner, B. F. (1939) · The American Journal of Psychology · doi:10.2307/1416495 · Cited by 4,388 · see citing works
Wiley Series in Probability and Statistics
(2008) · Wiley series in probability and statistics · doi:10.1002/series1345 · Cited by 4,270 · see citing works
LaValle, S. M. (2006) · Cited by 4,174 · see citing works
LaValle, S. (2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546877 · Cited by 4,004 · see citing works
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718 · see citing works
Design and use paradigms for gazebo, an open-source multi-robot simulator
Koenig, N., & Howard, A. (2005) · 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) · doi:10.1109/iros.2004.1389727 · Cited by 3,638 · see citing works
Microscopic Traffic Simulation using SUMO
López, P. Á., Wießner, E., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., & Wagner, P. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569938 · Cited by 3,607 · see citing works
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318 · see citing works
Dueling Network Architectures for Deep Reinforcement Learning
Wang, Z., Schaul, T., Hessel, M., van Hasselt, H., Lanctot, M., & de Freitas, N. (2015) · arXiv · doi:10.48550/arxiv.1511.06581 · Cited by 3,300 · see citing works
Deterministic Policy Gradient Algorithms
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., & Riedmiller, M. (2014) · HAL · Cited by 3,275 · see citing works
Randomized Kinodynamic Planning
LaValle, S. M., & Kuffner, J. J. (2001) · The International Journal of Robotics Research · doi:10.1177/02783640122067453 · Cited by 3,254 · see citing works
End to End Learning for Self-Driving Cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016) · arXiv · doi:10.48550/arxiv.1604.07316 · Cited by 3,198 · see citing works
Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv · doi:10.48550/arxiv.1511.05952 · Cited by 3,120 · see citing works
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119 · see citing works
Apprenticeship learning via inverse reinforcement learning
Abbeel, P., & Ng, A. Y. (2004) · doi:10.1145/1015330.1015430 · Cited by 2,873 · see citing works
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
Paden, B. A., Čáp, M., Yong, S. Z., Yershov, D. S., & Frazzoli, E. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2016.2578706 · Cited by 2,601 · see citing works
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Shah, S., Dey, D., Lovett, C., & Kapoor, A. (2018) · Springer Proceedings in Advanced Robotics · doi:10.1007/978-3-319-67361-5_40 · Cited by 2,254 · see citing works
Soft Actor-Critic Algorithms and Applications
Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., et al. (2018) · arXiv · doi:10.48550/arxiv.1812.05905 · Cited by 1,994 · see citing works
Generative adversarial imitation learning
Ho, J., & Ermon, S. (2016) · arXiv · doi:10.48550/arxiv.1606.03476 · Cited by 1,942 · see citing works
Curiosity-Driven Exploration by Self-Supervised Prediction
Pathak, D., Agrawal, P., Efros, A. A., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.70 · Cited by 1,815 · see citing works
Deep Reinforcement Learning That Matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11694 · Cited by 1,685 · see citing works
(2000) · doi:10.2460/ajvr.67.2.323 · Cited by 1,621 · see citing works
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., & Krishnan, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.18 · Cited by 1,603 · see citing works
Recurrent World Models Facilitate Policy EvolutionPreprint
Ha, D. R., & Schmidhuber, J. (2018) · Neural Information Processing Systems · doi:10.48550/arxiv.1809.01999 · Cited by 1,592 · see citing works
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
Ng, A. Y., Harada, D., & Russell, S. (1999) · Cited by 1,586 · see citing works
Transfer Learning for Reinforcement Learning Domains: A Survey
Taylor, M. E., & Stone, P. (2009) · doi:10.5555/1577069.1755839 · Cited by 1,521 · see citing works
On-line Q-learning Using Connectionist Systems
Rummery, G. A. (1994) · Cited by 1,475 · see citing works
On First-Order Meta-Learning Algorithms
Nichol, A., Achiam, J., & Schulman, J. (2018) · arXiv · doi:10.48550/arxiv.1803.02999 · Cited by 1,438 · see citing works
Sutton, R. S. (1990) · Elsevier eBooks · doi:10.1016/b978-1-55860-141-3.50030-4 · Cited by 1,348 · see citing works
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Peng, X. B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460528 · Cited by 1,309 · cited 1× · see citing works
“Authors in [118], randomized the dynamics of the simulator during training.”
BackgroundDeep Recurrent Q-Learning for Partially Observable MDPsPreprint
Hausknecht, M., & Stone, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.06527 · Cited by 1,224 · see citing works
(2015) · Journal of Machine Learning Research · Cited by 1,119 · see citing works
Deep Reinforcement Learning framework for Autonomous Driving
Sallab, A. E., Abdou, M., Perot, E., & Yogamani, S. (2017) · Electronic Imaging · doi:10.2352/issn.2470-1173.2017.19.avm-023 · Cited by 1,045 · see citing works
Self-citationRecent Advances in Hierarchical Reinforcement Learning
Barto, A. G., & Mahadevan, S. (2003) · Discrete Event Dynamic Systems · doi:10.1023/a:1022140919877 · Cited by 1,043 · see citing works
Planning and Decision-Making for Autonomous Vehicles
Schwarting, W., Alonso-Mora, J., & Rus, D. (2018) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105157 · Cited by 972 · see citing works
Real-Time Motion Planning With Applications to Autonomous Urban Driving
Kuwata, Y., Karaman, S., Teo, J., Frazzoli, E., How, J. P., & Fiore, G. A. (2009) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2008.2012116 · Cited by 896 · see citing works
Deep Q-learning From Demonstrations
Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Horgan, D., Quan, J., Sendonaris, A., Osband, I., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11757 · Cited by 889 · see citing works
End-to-End Learning of Driving Models from Large-Scale Video Datasets
Xu, H., Gao, Y., Yu, F., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.376 · Cited by 884 · see citing works
(2013) · Routledge eBooks · doi:10.4324/9781315017013-12 · Cited by 867 · see citing works
TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play
Tesauro, G. J. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.215 · Cited by 803 · see citing works
Multi-agent Reinforcement Learning: An Overview
Buşoniu, L., Babuška, R., & Schutter, B. D. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-14435-6_7 · Cited by 749 · see citing works
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Bansal, M., Krizhevsky, A., & Ogale, A. S. (2019) · Robotics: Science and Systems · doi:10.15607/rss.2019.xv.031 · Cited by 743 · see citing works
Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J.-M., Lam, V.-D., Bewley, A., & Shah, A. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793742 · Cited by 694 · see citing works
Efficient Training of Artificial Neural Networks for Autonomous Navigation
Pomerleau, D. (1991) · Neural Computation · doi:10.1162/neco.1991.3.1.88 · Cited by 693 · see citing works
A Tour of Reinforcement Learning: The View from Continuous Control
Recht, B. (2018) · Annual Review of Control Robotics and Autonomous Systems · doi:10.1146/annurev-control-053018-023825 · Cited by 642 · see citing works
Reinforcement Learning with Deep Energy-Based PoliciesPreprint
Haarnoja, T., Tang, H., Abbeel, P., & Levine, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08165 · Cited by 638 · see citing works
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., Kalakrishnan, M., Downs, L., Ibarz, J., Pastor, P., Konolige, K., et al. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8460875 · Cited by 636 · see citing works
Unity: A General Platform for Intelligent Agents
Juliani, A., Berges, V.-P., Teng, E., Cohen, A., Harper, J., Elion, C., Goy, C., Gao, Y., Henry, H., Mattar, M., et al. (2018) · arXiv · doi:10.48550/arxiv.1809.02627 · Cited by 626 · cited 1× · see citing works
“Moreover, Unity Machine Learning Agents Toolkit implements core RL algorithms, games, simulations environments for training RL or IL based agents [150].”
MethodIntrinsically Motivated Reinforcement Learning
Singh, S. P., Barto, A. G., & Chentanez, N. (2005) · doi:10.21236/ada440280 · Cited by 614 · see citing works
Learning to reinforcement learn
Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D., & Botvinick, M. (2016) · arXiv · doi:10.48550/arxiv.1611.05763 · Cited by 579 · see citing works
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
Shalev-Shwartz, S., Shammah, S., & Shashua, A. (2016) · arXiv · doi:10.48550/arxiv.1610.03295 · Cited by 509 · see citing works
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning
Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., & Abbeel, P. (2016) · arXiv · doi:10.48550/arxiv.1611.02779 · Cited by 503 · see citing works
Seeger, M. (2000) · Applied Physics Letters · doi:10.1162/153244303765208377 · Cited by 492 · see citing works
A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
Rosique, F., Navarro, P. J., Fernández, C., & Padilla, A. (2019) · Sensors · doi:10.3390/s19030648 · Cited by 469 · see citing works
Learning driving styles for autonomous vehicles from demonstration
Kuderer, M., Gulati, S., & Burgard, W. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139555 · Cited by 468 · see citing works
A Survey of Multi-Objective Sequential Decision-Making
Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3987 · Cited by 461 · see citing works
Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a CarPreprint
Bojarski, M., Yeres, P., Choromanska, A., Choromański, K., Firner, B., Jackel, L. D., & Müller, U. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.07911 · Cited by 414 · see citing works
Large-Scale Study of Curiosity-Driven Learning
Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., & Efros, A. A. (2018) · arXiv · doi:10.48550/arxiv.1808.04355 · Cited by 401 · see citing works
Zhan, W., Sun, L., Wang, D., Shi, H., Clausse, A., Naumann, M., Kümmerle, J., Königshof, H., Stiller, C., Fortelle, A. D. L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.03088 · Cited by 383 · see citing works
Navigating Occluded Intersections with Autonomous Vehicles Using Deep Reinforcement Learning
Isele, D., Rahimi, R., Cosgun, A., Subramanian, K., & Fujimura, K. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8461233 · Cited by 379 · see citing works
Efficient Reductions for Imitation Learning
Ross, S., & Bagnell, D. (2018) · doi:10.1184/r1/6554699 · Cited by 366 · see citing works
Deep Reinforcement Learning that MattersPreprint
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.06560 · Cited by 340 · see citing works
Virtual to Real Reinforcement Learning for Autonomous Driving
Pan, X., You, Y., Wang, Z., & Lu, C. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.11 · Cited by 326 · see citing works
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
Randløv, J., & Alstrøm, P. (1998) · Cited by 324 · see citing works
Model-free Deep Reinforcement Learning for Urban Autonomous Driving
Chen, J., Yuan, B., & Tomizuka, M. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917306 · Cited by 320 · see citing works
A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers
Wang, P., Chan, C., & Fortelle, A. D. L. (2018) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2018.8500556 · Cited by 319 · see citing works
Language Understanding for Text-based Games using Deep Reinforcement Learning
Narasimhan, K., Kulkarni, T. D., & Barzilay, R. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1001 · Cited by 304 · see citing works
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00940 · Cited by 303 · cited 1× · see citing works
“into a multi-task model [8], [9].”
Self-citationState representation learning for control: An overview
Lesort, T., Díaz-Rodríguez, N., Goudou, J.-F., & Filliat, D. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.006 · Cited by 302 · see citing works
TORCS, The Open Racing Car Simulator
Espié, E., Guionneau, C., Wymann, B., Christos Dimitrakakis, A., Coulom, R., & Sumner, A. J. (2005) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 294 · see citing works
Deep Q-learning from DemonstrationsPreprint
Hester, T., Vecerík, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Horgan, D., Quan, J., Sendonaris, A., Dulac-Arnold, G., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.03732 · Cited by 276 · see citing works
Simple random search provides a competitive approach to reinforcement learningPreprint
Mania, H., Guy, A., & Recht, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.07055 · Cited by 251 · see citing works
Exploration and apprenticeship learning in reinforcement learning
Abbeel, P., & Ng, A. Y. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102352 · Cited by 224 · see citing works
Pre-Crash Scenario Typology for Crash Avoidance Research
Najm, W. G., Smith, J. D., & Yanagisawa, M. (2007) · Cited by 203 · see citing works
Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges
Siam, M., Elkerdawy, S., Jagersand, M., & Yogamani, S. (2017) · 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317714 · Cited by 178 · see citing works
Self-citationSimple random search of static linear policies is competitive for reinforcement learning
Mania, H., Guy, A., & Recht, B. (2018) · neural information processing systems · Cited by 163 · see citing works
Autonomous braking system via deep reinforcement learning
Chae, H., Kang, C. M., Kim, B., Kim, J., Chung, C. C., & Choi, J. W. (2017) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317839 · Cited by 162 · see citing works
Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge
Wang, P., & Chan, C. (2017) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317735 · Cited by 151 · see citing works
Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control.Preprint
Wu, C., Kreidieh, A., Parvate, K., Vinitsky, E., & Bayen, A. M. (2017) · arXiv (Cornell University) · Cited by 149 · see citing works
Yu, C., Wang, X., Xu, X., et al. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2893683 · Cited by 148 · see citing works
Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning
Palanisamy, P. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207663 · Cited by 148 · see citing works
End-to-End Deep Reinforcement Learning for Lane Keeping AssistPreprint
Sallab, A. E., Abdou, M., Perot, E., & Yogamani, S. K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.04340 · Cited by 146 · see citing works
Self-citationMulti-objective multi-agent decision making: a utility-based analysis and survey
Rădulescu, R., Mannion, P., Roijers, D. M., & Nowé, A. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09433-x · Cited by 138 · see citing works
Self-citationMitchell, T. M., Carbonell, J. G., & Michalski, R. S. (1986) · Kluwer international series in engineering and computer science · doi:10.1007/978-1-4613-2279-5 · Cited by 136 · see citing works
A Multiple-Goal Reinforcement Learning Method for Complex Vehicle Overtaking Maneuvers
Ngai, D. C. K., & Yung, N. H. C. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2106158 · Cited by 131 · see citing works
Recurrent Environment SimulatorsPreprint
Chiappa, S., Racanière, S., Wierstra, D., & Mohamed, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.02254 · Cited by 130 · see citing works
Generating Adversarial Driving Scenarios in High-Fidelity Simulators
Abeysirigoonawardena, Y., Shkurti, F., & Dudek, G. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793740 · Cited by 130 · see citing works
RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
El Madawi, K., Rashed, H., El Sallab, A., Nasr, O., Kamel, H., & Yogamani, S. (2019) · 2019 IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917447 · Cited by 116 · see citing works
Self-citationQuery-Efficient Imitation Learning for End-to-End Simulated Driving
Zhang, J., & Cho, K. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10857 · Cited by 111 · see citing works
Sample-based learning and search with permanent and transient memories
Silver, D., Sutton, R. S., & Müller, M. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390278 · Cited by 110 · see citing works
VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control
Zhang, J., Tai, L., Yun, P., Xiong, Y., Liu, M., Boedecker, J., & Burgard, W. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2894216 · Cited by 109 · cited 1× · see citing works
“Similarly, [126] performs domain adaptation to map real world images to simulated images.”
BackgroundVisual SLAM for Automated Driving: Exploring the Applications of Deep Learning
Milz, S., Arbeiter, G., Witt, C., Abdallah, B., & Yogamani, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00062 · Cited by 107 · see citing works
Self-citationLearning to Drive from Simulation without Real World Labels
Bewley, A., Rigley, J., Liu, Y., Hawke, J., Shen, R., Lam, V.-D., & Kendall, A. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793668 · Cited by 103 · see citing works
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the WorstPreprint
Bansal, M., Krizhevsky, A., & Ogale, A. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.03079 · Cited by 102 · see citing works
Policy Optimization with Demonstrations
Kang, B., Jie, Z., & Feng, J. (2018) · International Conference on Machine Learning · Cited by 101 · see citing works
Collective intelligence for control of distributed dynamical systems
Wolpert, D. H., Wheeler, K., & Tumer, K. (2000) · Europhysics Letters (EPL) · doi:10.1209/epl/i2000-00208-x · Cited by 97 · see citing works
MODNet: Motion and Appearance based Moving Object Detection Network for Autonomous Driving
Siam, M., Mahgoub, H., Zahran, M., Yogamani, S., Jagersand, M., & El-Sallab, A. (2018) · 2018 21st International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569744 · Cited by 95 · see citing works
Self-citationFrom Pixels to Torques: Policy Learning with Deep Dynamical ModelsPreprint
Wahlström, N., Schön, T. B., & Deisenroth, M. P. (2015) · arXiv (Cornell University) · doi:10.25561/21120 · Cited by 78 · see citing works
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Hong, Z.-W., Shann, T.-Y., Su, S.-Y., Chang, Y.-H., & Lee, C.-Y. (2018) · arXiv · doi:10.48550/arxiv.1802.04564 · Cited by 77 · see citing works
Exploring Applications of Deep Reinforcement Learning for Real-world Autonomous Driving Systems
Talpaert, V., Sobh, I., Kiran, B. R., Mannion, P., Yogamani, S. K., El-Sallab, A., & Pérez, P. (2019) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0007520305640572 · Cited by 75 · see citing works
Self-citationLearning to Drive using Inverse Reinforcement Learning and Deep Q-NetworksPreprint
Sharifzadeh, S., Chiotellis, I., Triebel, R., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03653 · Cited by 71 · see citing works
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorchPreprint
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Combining Deep Reinforcement Learning and Safety Based Control for Autonomous DrivingPreprint
Xiong, X., Wang, J., Zhang, F., & Li, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00147 · Cited by 66 · see citing works
Wachi, A. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/832 · Cited by 64 · see citing works
Reinforcement learning with multi-fidelity simulators
Cutler, M., Walsh, T. J., & How, J. P. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907423 · Cited by 60 · see citing works
Learning deep dynamical models from image pixels
Wahlström, N., Schön, T. B., & Deisenroth, M. P. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.12.271 · Cited by 60 · see citing works
Deep Multi Agent Reinforcement Learning for Autonomous Driving
Bhalla, S., Subramanian, S. G., & Crowley, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47358-7_7 · Cited by 59 · see citing works
Behaviour Suite for Reinforcement LearningPreprint
Osband, I., Doron, Y., Hessel, M., Aslanides, J., Sezener, E., Saraiva, A., McKinney, K., Lattimore, T., Szepesvári, C., Singh, S. P., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03568 · Cited by 58 · see citing works
SoilingNet: Soiling Detection on Automotive Surround-View Cameras
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Self-citationYes, we GAN: Applying adversarial techniques for autonomous driving
Uřičář, M., Křížek, P., Hurych, D., Sobh, I., Yogamani, S. K., & Denny, P. E. (2019) · Electronic Imaging · doi:10.2352/issn.2470-1173.2019.15.avm-048 · Cited by 54 · see citing works
Self-citationPolicy invariance under reward transformations for multi-objective reinforcement learning
Mannion, P., Devlin, S., Mason, K., Duggan, J., & Howley, E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.090 · Cited by 52 · see citing works
Self-citationBöhmer, W., Springenberg, J. T., Boedecker, J., Riedmiller, M., & Obermayer, K. (2015) · Künstliche Intell. · doi:10.1007/s13218-015-0356-1 · Cited by 51 · see citing works
Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision
Kumar, V. R., Milz, S., Witt, C., Simón, M., Amende, K., Petzold, J., Yogamani, S. K., & Pech, T. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569665 · Cited by 42 · see citing works
Self-citationCaspi, I., Leibovich, G., Endrawis, S., et al. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1134899 · Cited by 40 · see citing works
Raffin, A., Hill, A., Traoré, R., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.08651 · Cited by 40 · see citing works
NeurAll: Towards a Unified Visual Perception Model for Automated Driving
Sistu, G., Leang, I., Chennupati, S., Yogamani, S. K., Eising, C., Milz, S., & Rawashdeh, S. A. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917043 · Cited by 30 · see citing works
Self-citationRGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous DrivingPreprint
Madawy, K. E., Rashed, H., Sallab, A. E., Nasr, O. A., Kamel, H., & Yogamani, S. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.00208 · Cited by 23 · see citing works
Self-citationTransferring Autonomous Driving Knowledge on Simulated and Real IntersectionsPreprint
Isele, D., & Cosgun, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.01106 · Cited by 22 · see citing works
Conditional Vehicle Trajectories Prediction in CARLA Urban Environment
Buhet, T., Wirbel, É., & Perrotton, X. (2019) · doi:10.1109/iccvw.2019.00284 · Cited by 18 · see citing works
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement LearningPreprint
Liaw, R., Krishnan, S., Garg, A., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.01503 · Cited by 17 · see citing works
A Survey of State-Action Representations for Autonomous Driving
Leurent, E. (2018) · LillOA (Université de Lille (University Of Lille)) · Cited by 15 · see citing works
Autonomous vehicle control via deep reinforcement learning
Kardell, S., & Kuosku, M. (2017) · Chalmers Publication Library (Chalmers University of Technology) · Cited by 13 · see citing works
Ye, C., Ma, H., Zhang, X., Zhang, K., & You, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71589-6_36 · Cited by 12 · see citing works
Reinforcement Learning with A* and a Deep HeuristicPreprint
Keselman, A., Ten, S., Ghazali, A., & Jubeh, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.07745 · Cited by 12 · see citing works
An Evolutionary Game Theoretic Analysis of Difference Evaluation Functions
Colby, M. K., & Tumer, K. (2015) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2739480.2754770 · Cited by 11 · see citing works
A Theoretical and Empirical Analysis of Reward Transformations in Multi-Objective Stochastic Games
Mannion, P., Duggan, J., & Howley, E. (2017) · Research@THEA · doi:10.5555/3091125.3091384 · Cited by 10 · see citing works
Self-citationQuiter, C., & Ernst, M. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1248998 · Cited by 5 · see citing works
Wiewiora, E. (2017) · Encyclopedia of Machine Learning and Data Mining · doi:10.1007/978-1-4899-7687-1_966 · Cited by 3 · see citing works
A Tempt to Unify Heterogeneous Driving Databases using Traffic PrimitivesPreprint
Zhu, J., Wang, W., & Zhao, D. X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.04925 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 19
- R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction (Second Edition). MIT Press, 2018. 1, 4, 5, 6
- S. Kuutti, R. Bowden, Y. Jin, P. Barber, and S. Fallah, "A survey of deep learning applications to autonomous vehicle control," IEEE Transactions on Intelligent Transportation Systems, 2020. 3
- M. Wiering and M. van Otterlo, Eds., Reinforcement Learning: State- of-the-Art. Springer, 2012. 4
- V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, "Asynchronous methods for deep rein- forcement learning," in International Conference on Machine Learning, 2016. 5
- R. I. Brafman and M. Tennenholtz, "R-max-a general polynomial time algorithm for near-optimal reinforcement learning," Journal of Machine Learning Research, vol. 3, no. Oct, 2002. 6
- S. Devlin and D. Kudenko, "Theoretical considerations of potential- based reward shaping for multi-agent systems," in Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2011. 8
- P. Mannion, K. Mason, S. Devlin, J. Duggan, and E. Howley, "Multi- objective dynamic dispatch optimisation using multi-agent reinforce- ment learning," in Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016. 8
- K. Mason, P. Mannion, J. Duggan, and E. Howley, "Applying multi- agent reinforcement learning to watershed management," in Proceed- ings of the Adaptive and Learning Agents workshop (at AAMAS 2016), 2016. 8
- V. Pareto, Manual of political economy. OUP Oxford, 1906. 8
- B. Kang, Z. Jie, and J. Feng, "Policy optimization with demonstra- tions," in International Conference on Machine Learning, 2018, pp. 2474-2483. 9
- S. Ibrahim and D. Nevin, "End-to-end framework for fast learning asynchronous agents," in the 32nd Conference on Neural Information Processing Systems, Imitation Learning and its Challenges in Robotics workshop, 2018. 9
- I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets," in Advances in Neural Information Processing Systems 27, 2014, pp. 2672-2680. 9
- C. Li and K. Czarnecki, "Urban driving with multi-objective deep reinforcement learning," in Proceedings of the 18th International Con- ference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 2019, pp. 359-367. 9, 10
- M. Watter, J. Springenberg, J. Boedecker, and M. Riedmiller, "Embed to control: A locally linear latent dynamics model for control from raw images," in Advances in neural information processing systems, 2015. 10
- D. A. Pomerleau, "Alvinn: An autonomous land vehicle in a neural network," in Advances in neural information processing systems, 1989. 10
- Z. Wang, V. Bapst, N. Heess, V. Mnih, R. Munos, K. Kavukcuoglu, and N. de Freitas, "Sample efficient actor-critic with experience replay," in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017. 12
- M. Al-Shedivat, T. Bansal, Y. Burda, I. Sutskever, I. Mordatch, and P. Abbeel, "Continuous adaptation via meta-learning in nonstationary and competitive environments," in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 -May 3, 2018, Conference Track Proceedings. OpenReview.net, 2018. 12
- A. Y. Ng, D. Harada, and S. Russell, "Policy invariance under reward transformations: Theory and application to reward shaping," in ICML, vol. 99, 1999, pp. 278-287. 13
- P. Abbeel and A. Y. Ng, "Apprenticeship learning via inverse rein- forcement learning," in Proceedings of the Twenty-first International Conference on Machine Learning, ser. ICML '04. ACM, 2004. 13
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- Authors
- Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Perez, P.
- Affiliations
- Navya, Courbevoie, France
- Valeo Cairo AI Team, Giza, Egypt
- École Nationale Supérieure de Techniques Avancées (FR)
- Ollscoil na Gaillimhe – University of Galway (IE)
- Valeo (Ireland) (IE)
- Valeo.ai, Paris, France
- ORCID
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-06
- Volume
- 23
- Issue
- 6
- Pages
- 4909-4926
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3003533476
- Cited by
- 2,342
- References
- 146
- Citations by year
- 2026: 354 · 2025: 573 · 2024: 542 · 2023: 435 · 2022: 258 · 2021: 157 · 2020: 23
- FWCI
- 122.51
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Keywords
- deep reinforcement learning, autonomous driving, reinforcement learning algorithms, behavior cloning, imitation learning, inverse reinforcement learning, driving simulator, autonomous driving validation, robustness, field deployment
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