Y. Abbasi-yadkori and C. Szepesvári, Regret bounds for the adaptive control of linear quadratic systems, Proceedings of the 24th Annual Conference on Learning Theory, vol.19, pp.9-11, 2011.

Y. Abbasi-yadkori, D. Pál, C. Szepesvári, J. Shawe-taylor, R. S. Zemel et al., Stochastic Linear Bandits, Bandit Algorithms, vol.24, pp.205-218, 2020.

M. Abeille and A. Lazaric, Quadratic Optimization Problems, Linear Algebra and Optimization with Applications to Machine Learning, vol.80, pp.167-190, 2020.

V. Adetola, D. Dehaan, and M. Guay, Adaptive model predictive control for constrained nonlinear systems, Systems & Control Letters, vol.58, issue.5, pp.320-326, 2009.

B. Amos, I. D. Rodriguez, J. Sacks, B. Boots, Z. Kolter et al., Differentiable MPC for end-to-end planning and control, Advances in Neural Information Processing Systems, 2018.

A. Aswani, H. Gonzalez, S. S. Sastry, and C. Tomlin, Provably safe and robust learning-based model predictive control, Automatica, vol.49, issue.5, pp.1216-1226, 2013.

T. Basar and P. Bernhard, H/sup ?/-0ptimal Control and Related Minimax Design Problems: A Dynamic Game Approach, IEEE Transactions on Automatic Control, vol.41, issue.9, p.1397, 1996.

A. Ben-tal, L. El-ghaoui, and A. Nemirovski, Robust Optimization, vol.28, 2009.

D. Bertsimas, D. B. Brown, and C. Caramanis, Theory and applications of robust optimization, SIAM review, vol.53, issue.3, pp.464-501, 2011.

L. Busoniu, E. Pall, and R. Munos, Continuous-action planning for discounted infinite-horizon nonlinear optimal control with lipschitz values, Automatica, vol.92, pp.100-108, 2018.

S. Dean, H. Mania, N. Matni, B. Recht, and S. Tu, On the Sample Complexity of the Linear Quadratic Regulator, Foundations of Computational Mathematics, vol.20, issue.4, pp.633-679, 2019.

S. Dean, H. Mania, N. Matni, B. Recht, and S. Tu, Regret bounds for robust adaptive control of the linear quadratic regulator, Advances in Neural Information Processing Systems, vol.31, pp.4188-4197, 2018.

V. Delos and D. Teissandier, Minkowski Sum of Polytopes Defined by Their Vertices, Journal of Applied Mathematics and Physics (JAMP), vol.3, issue.1, pp.62-67, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01092040

D. Efimov, L. Fridman, T. Raïssi, A. Zolghadri, and R. Seydou, Interval estimation for LPV systems applying high order sliding mode techniques, Automatica, vol.48, issue.9, pp.2365-2371, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00701643

D. Efimov, T. Raïssi, S. Chebotarev, and A. Zolghadri, Interval state observer for nonlinear time varying systems, Automatica, vol.49, issue.1, pp.200-205, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00719911

M. K. Faradonbeh, A. Tewari, and G. Michailidis, Finite time analysis of optimal adaptive policies for linear-quadratic systems, CoRR, 2017.

H. Fukushima, T. H. Kim, and T. Sugie, Adaptive model predictive control for a class of constrained linear systems based on the comparison model, Automatica, 2007.

B. L. Gorissen, ?. Yan?koglu, and D. Hertog, A practical guide to robust optimization, Omega, vol.53, pp.124-137, 2015.

J. Hren and R. Munos, Optimistic planning of deterministic systems, European Workshop on Reinforcement Learning, pp.151-164, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00830182

M. Ibrahimi, A. Javanmard, R. , and B. , Efficient reinforcement learning for high dimensional linear quadratic systems, Advances in Neural Information Processing Systems, vol.4, 2013.

G. N. Iyengar, Robust Dynamic Programming. Mathematics of Operations Research, vol.30, pp.257-280, 2005.

J. Köhler, E. Andina, R. Soloperto, M. A. Müller, A. et al., Linear robust adaptive model predictive control: Computational complexity and conservatism, 2019 IEEE 58th Conference on Decision and Control (CDC), pp.1383-1388, 2019.

E. V. Kumar and J. Jerome, Robust lqr controller design for stabilizing and trajectory tracking of inverted pendulum, International Conference on Design and Manufacturing, vol.64, pp.169-178, 2013.

I. Lenz, R. A. Knepper, A. Saxena, and . Deepmpc, Learning deep latent features for model predictive control, Robotics: Science and Systems, 2015.

E. Leurent, An environment for autonomous driving decision-making, 2018.

E. Leurent and J. Mercat, Social attention for autonomous decision-making in dense traffic, Machine Learning for Autonomous Driving Workshop at NeurIPS, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02383940

E. Leurent, D. Efimov, T. Raïssi, and W. Perruquetti, Interval prediction for continuous-time systems with parametric uncertainties, Proc. IEEE Conference on Decision and Control (CDC), 2019.
URL : https://hal.archives-ouvertes.fr/hal-02383571

E. Leurent, D. Efimov, and O. Maillard, Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems, 2020 IEEE 59th Conference on Decision and Control (CDC), pp.8-11, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02942414

S. Levine, C. Finn, T. Darrell, A. , and P. , End-to-end training of deep visuomotor policies, 2015.

M. Lorenzen, F. Allgöwer, and M. Cannon, Adaptive model predictive control with robust constraint satisfaction, vol.50, pp.3313-3318, 2017.

X. Lu and M. Cannon, Robust adaptive tube model predictive control, Proceedings of the American Control Conference, vol.9781538679265, 2019.

X. Lu and S. K. Spurgeon, Robust sliding mode control of uncertain nonlinear systems, Systems & Control Letters, vol.32, issue.2, pp.75-90, 1997.

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness et al., Human-level control through deep reinforcement learning, Nature, vol.518, issue.7540, pp.529-533, 2015.

A. Nilim and L. El-ghaoui, Robust Control of Markov Decision Processes with Uncertain Transition Matrices, Operations Research, vol.53, pp.780-798, 2005.

Y. Ouyang, M. Gagrani, and R. Jain, Learning-based control of unknown linear systems with thompson sampling, 2017.

V. H. Peña, T. L. Lai, and Q. Shao, Self-normalized processes: Limit theory and Statistical Applications, 2008.

U. Rosolia and F. Borrelli, Sample-Based Learning Model Predictive Control for Linear Uncertain Systems, Proceedings of the IEEE Conference on Decision and Control, 2019.

S. Sastry, M. Bodson, and J. F. Bartram, Adaptive Control: Stability, Convergence, and Robustness. The Journal of the, 1990.

J. Schneider, Exploiting model uncertainty estimates for safe dynamic control learning. Advances in neural information processing systems, pp.1047-1053, 1997.

D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai et al., A general reinforcement learning algorithm that masters chess, shogi, and go through self-play, Science, vol.362, issue.6419, pp.1140-1144, 2018.

M. Tanaskovic, L. Fagiano, R. Smith, and M. Morari, Adaptive receding horizon control for constrained MIMO systems, Automatica, 2014.

M. Turchetta, F. Berkenkamp, and A. Krause, Safe exploration in finite Markov decision processes with Gaussian processes, Advances in Neural Information Processing Systems, 2016.

A. Weinstein and M. Littman, Bandit-based planning and learning in continuous-action markov decision processes, Proceedings of the 22nd International Conference on Automated Planning and Scheduling, pp.306-314, 2012.

W. Wiesemann, D. Kuhn, and B. Rustem, Robust Markov Decision Processes. Mathematics of Operations Research, pp.1-52, 2013.