[60] I. Osband and B. Van Roy. Gaussian-dirichlet posterior dominance in sequential learning. arXiv preprint arXiv:1702.04126, 2017.
[61] I. Osband and B. Van Roy. Why is posterior sampling better than optimism for reinforcement learning? In International conference on machine learning, pages 2701-2710. PMLR, 2017.
[62] I. Osband, B. Van Roy, D. J. Russo, and Z. Wen. Deep exploration via randomized value functions. Journal of Machine Learning Research, 20:1-62, 2019.
[63] G. Ostrovski, M. G. Bellemare, A. Oord, and R. Munos. Count-based exploration with neural density models. In International conference on machine learning, pages 2721-2730. PMLR, 2017.
[64] D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell. Curiosity-driven exploration by self-supervised prediction. In International conference on machine learning, pages 2778-2787. PMLR, 2017.
[65] J. Peters, K. Mulling, and Y. Altun. Relative entropy policy search. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 24, pages 1607-1612, 2010.
[66] M. L. Puterman. Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons, 2014.
[67] J. Qian, R. Fruit, M. Pirotta, and A. Lazaric. Concentration inequalities for multinoulli random variables. arXiv preprint arXiv:2001.11595, 2020.
[68] K. Rawlik, M. Toussaint, and S. Vijayakumar. On stochastic optimal control and reinforcement learning by approximate inference. Proceedings of Robotics: Science and Systems VIII, 2012.
[69] D. Russo and B. Van Roy. Learning to optimize via information-directed sampling. Advances in Neural Information Processing Systems, 27, 2014.
[70] D. Russo and B. Van Roy. An information-theoretic analysis of thompson sampling. The Journal of Machine Learning Research, 17(1):2442-2471, 2016.
[71] D. J. Russo, B. Van Roy, A. Kazerouni, I. Osband, Z. Wen, et al. A tutorial on thompson sampling. Foundations and Trends® in Machine Learning, 11(1):1-96, 2018.
[72] T. Schaul, J. Quan, I. Antonoglou, and D. Silver. Prioritized experience replay. arXiv preprint arXiv:1511.05952, 2015.
[73] M. J. Schervish. Theory of statistics. Springer Science & Business Media, 2012.
[74] S. A. Serrano. Algorithms for unsymmetric cone optimization and an implementation for problems with the exponential cone. PhD thesis, Stanford University, 2015.
[75] M. Strens. A bayesian framework for reinforcement learning. In ICML, volume 2000, pages 943-950, 2000.
[76] R. S. Sutton and A. G. Barto. Reinforcement learning: An introduction. MIT press, 2018.
[77] J. Tarbouriech and A. Lazaric. Active exploration in markov decision processes. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 974–982. PMLR, 2019.
[78] W. R. Thompson. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, 25(3-4):285-294, 1933.
[79] D. Tiapkin, D. Belomestny, D. Calandriello, E. Moulines, R. Munos, A. Naumov, P. Perrault, Y. Tang, M. Valko, and P. Menard. Fast rates for maximum entropy exploration. arXiv preprint arXiv:2303.08059, 2023.
[80] E. Todorov. Linearly-solvable markov decision problems. Advances in neural information processing systems, 19, 2006.
[81] E. Todorov. Efficient computation of optimal actions. Proceedings of the national academy of sciences, 106(28):11478-11483, 2009.