D. P. Bertsekas and J. N. Tsitsiklis, Neuro-dynamic programming, Athena Scientific, vol.3, 1996.
DOI : 10.1007/0-306-48332-7_333

R. S. Sutton and A. G. Barto, Reinforcement learning: An Introduction, ser. Adaptive computation and machine learning, 1998.
DOI : 10.1007/978-1-4615-3618-5

J. Randløv and P. Alstrøm, Learning to Drive a Bicycle Using Reinforcement Learning and Shaping, Proceedings of the Fifteenth International Conference on Machine Learning, pp.463-471, 1998.

A. Y. Ng, D. Harada, and S. J. Russell, Policy invariance under reward transformations: Theory and application to reward shaping, Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), pp.278-287, 1999.

J. Kober, J. A. Bagnell, and J. Peters, Reinforcement learning in robotics: A survey, 2013.

B. Argall, S. Chernova, M. M. Veloso, and B. Browning, A survey of robot learning from demonstration, Robotics and Autonomous Systems, vol.57, issue.5, pp.469-483, 2009.
DOI : 10.1016/j.robot.2008.10.024

D. A. Pomerleau, Efficient Training of Artificial Neural Networks for Autonomous Navigation, Neural Computation, vol.3, issue.1, pp.88-97, 1991.
DOI : 10.1162/neco.1989.1.4.541

P. Abbeel and A. Y. Ng, Apprenticeship learning via inverse reinforcement learning, Twenty-first international conference on Machine learning , ICML '04, 2004.
DOI : 10.1145/1015330.1015430

URL : http://www.aicml.cs.ualberta.ca/banff04/icml/pages/papers/335.pdf

A. Y. Ng and S. J. Russell, Algorithms for inverse reinforcement learning, Proceedings of the Seventeenth International Conference on Machine Learning, pp.663-670, 2000.

S. J. Russell, Learning agents for uncertain environments (extended abstract), Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.101-103, 1998.
DOI : 10.1145/279943.279964

URL : http://www.eecs.berkeley.edu/~russell/papers/colt98-uncertainty.pdf

P. Abbeel, A. Coates, and A. Y. Ng, Autonomous Helicopter Aerobatics through Apprenticeship Learning, The International Journal of Robotics Research, vol.20, issue.1, pp.1608-1639, 2010.
DOI : 10.1109/TASSP.1978.1163055

K. M. Kitani, B. D. Ziebart, J. A. Bagnell, and M. Hebert, Activity forecasting, Computer Vision -ECCV 2012 -12th European Conference on Computer Vision Proceedings, Part IV, pp.201-214, 2012.

P. Abbeel, D. Dolgov, A. Y. Ng, and S. Thrun, Apprenticeship learning for motion planning with application to parking lot navigation, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.1083-1090, 2008.
DOI : 10.1109/IROS.2008.4651222

C. Finn, S. Levine, and P. Abbeel, Guided cost learning: Deep inverse optimal control via policy optimization, Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, pp.49-58, 2016.

M. T. Wolf and J. W. Burdick, Artificial potential functions for highway driving with collision avoidance, 2008 IEEE International Conference on Robotics and Automation, pp.3731-3736, 2008.
DOI : 10.1109/ROBOT.2008.4543783

URL : https://authors.library.caltech.edu/19174/1/Wolf2008p88342008_Ieee_International_Conference_On_Robotics_And_Automation_Vols_1-9.pdf

M. Montemerlo, J. Becker, and S. Bhat, Junior: The stanford entry in the urban challenge, The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, pp.91-123, 2009.

J. Wei and J. M. Dolan, A robust autonomous freeway driving algorithm, 2009 IEEE Intelligent Vehicles Symposium, pp.1015-1020, 2009.
DOI : 10.1109/IVS.2009.5164420

D. Silver, J. A. Bagnell, and A. Stentz, High Performance Outdoor Navigation from Overhead Data using Imitation Learning, Robotics: Science and Systems IV, 2008.
DOI : 10.15607/RSS.2008.IV.034

URL : http://repository.cmu.edu/cgi/viewcontent.cgi?article=1049&context=robotics

M. Shimosaka, T. Kaneko, and K. Nishi, Modeling risk anticipation and defensive driving on residential roads with inverse reinforcement learning, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp.1694-1700, 2014.
DOI : 10.1109/ITSC.2014.6957937

D. , S. González, V. Romero-cano, J. Steeve-dibangoye, and C. Laugier, Interaction-Aware Driver Maneuver Inference in Highways Using Realistic Driver Models, Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems, 2017.

]. D. Sadigh, S. Sastry, S. A. Seshia, and A. D. Dragan, Planning for Autonomous Cars that Leverage Effects on Human Actions, Robotics: Science and Systems XII, 2016.
DOI : 10.15607/RSS.2016.XII.029

S. Levine and V. Koltun, Continuous Inverse Optimal Control with Locally Optimal Examples, ICML '12: Proceedings of the 29th International Conference on Machine Learning, 2012.

A. Byravan, M. Monfort, and B. D. Ziebart, Graph-based inverse optimal control for robot manipulation, Proceedings of the Twenty- Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, pp.1874-1880, 2015.

B. Okal and K. O. Arras, Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.2889-2895, 2016.
DOI : 10.1109/ICRA.2016.7487452

N. Ratliff, J. A. Bagnell, and M. Zinkevich, Maximum margin planning, Proceedings of the 23rd international conference on Machine learning , ICML '06, 2006.
DOI : 10.1145/1143844.1143936

URL : http://www-clmc.usc.edu/publications/R/ratliff-ICML2006.pdf

M. Kuderer, S. Gulati, and W. Burgard, Learning driving styles for autonomous vehicles from demonstration, 2015 IEEE International Conference on Robotics and Automation (ICRA), pp.2641-2646, 2015.
DOI : 10.1109/ICRA.2015.7139555

K. Shiarlis, J. Messias, and S. Whiteson, Rapidly exploring learning trees, 2017 IEEE International Conference on Robotics and Automation (ICRA), pp.1541-1548, 2017.
DOI : 10.1109/ICRA.2017.7989184

S. H. Lee and S. W. Seo, A learning-based framework for handling dilemmas in urban automated driving, 2017 IEEE International Conference on Robotics and Automation (ICRA), pp.1436-1442, 2017.
DOI : 10.1109/ICRA.2017.7989172

M. Shimosaka, J. Sato, K. Takenaka, and K. Hitomi, Fast inverse reinforcement learning with interval consistent graph for driving behavior prediction, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp.1532-1538, 2017.

M. Mcnaughton, C. Urmson, J. M. Dolan, and J. Lee, Motion planning for autonomous driving with a conformal spatiotemporal lattice, 2011 IEEE International Conference on Robotics and Automation, pp.9-13, 2011.
DOI : 10.1109/ICRA.2011.5980223

T. M. Howard, C. J. Green, A. Kelly, and D. Ferguson, State space sampling of feasible motions for high-performance mobile robot navigation in complex environments, Journal of Field Robotics, vol.24, issue.6-7, pp.6-7, 2008.
DOI : 10.1177/0278364902021010841

B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey, Maximum entropy inverse reinforcement learning, Proceedings of the Twenty- Third AAAI Conference on Artificial Intelligence, AAAI 2008, pp.1433-1438, 2008.

M. Pivtoraiko and A. Kelly, Efficient constrained path planning via search in state lattices, The 8th International Symposium on Artificial Intelligence, Robotics and Automation in Space, 2005.

A. Kelly and B. Nagy, Reactive Nonholonomic Trajectory Generation via Parametric Optimal Control, The International Journal of Robotics Research, vol.22, issue.7-8, pp.583-602, 2003.
DOI : 10.1109/9.384215

C. Guestrin and D. Ormoneit, Robust combination of local controllers, UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, pp.178-185, 2001.

G. Neumann, M. Pfeiffer, and W. Maass, Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs, pp.250-261, 2007.
DOI : 10.1007/978-3-540-74958-5_25

URL : https://link.springer.com/content/pdf/10.1007%2F978-3-540-74958-5_25.pdf

L. Rummelhard, A. , and C. Laugier, Conditional Monte Carlo Dense Occupancy Tracker, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp.2485-2490, 2015.
DOI : 10.1109/ITSC.2015.400

URL : https://hal.archives-ouvertes.fr/hal-01205298

M. Wulfmeier, D. Z. Wang, and I. Posner, Watch this: Scalable cost-function learning for path planning in urban environments, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.2089-2095, 2016.
DOI : 10.1109/IROS.2016.7759328

D. Vasquez, B. Okal, and K. O. Arras, Inverse Reinforcement Learning algorithms and features for robot navigation in crowds: An experimental comparison, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.1341-1346, 2014.
DOI : 10.1109/IROS.2014.6942731

URL : https://hal.archives-ouvertes.fr/hal-01105265

P. Henry, C. Vollmer, B. Ferris, and D. Fox, Learning to navigate through crowded environments, 2010 IEEE International Conference on Robotics and Automation, pp.981-986, 2010.
DOI : 10.1109/ROBOT.2010.5509772

URL : http://www.cs.washington.edu/ai/Mobile_Robotics/postscripts/crowd-navigation-icra-2010.pdf