[1] M. G. Azar, V. Gómez, and B. Kappen. Dynamic policy programming with function approximation. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, volume 15 of Proceedings of Machine Learning Research, pages 119-127, 2011.
[2] K. Dvijotham and E. Todorov. Inverse optimal control with linearly-solvable MDPs. In Proceedings of the 27th International conference on machine learning (ICML-10), pages 335–342, 2010.
[3] B. Eysenbach and S. Levine. Maximum entropy RL (provably) solves some robust RL problems. In International Conference on Learning Representations, 2022.
[4] H2RLab. pomdp.py. https://h2r.github.io/pomdp-py/html/, —.
[5] R. He, E. Brunskill, and N. Roy. PUMA: planning under uncertainty with macro-actions. In M. Fox and D. Poole, editors, Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11-15, 2010, pages 1089-1095. AAAI Press, 2010.
[6] M. Hoerger, H. Kurniawati, and A. Elfes. Multilevel Monte-Carlo for solving POMDPs online. International Journal of Robotics Research (IJRR), 2022.
[7] L. Kaelbling, M. Littman, and A. Cassandra. Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101(1-2):99-134, 1998.
[8] A. N. Kolmogorov and S. V. Fomin. Introductory Real Analysis. Dover, 1970.
[9] H. Kurniawati. Partially observed Markov decision processes and robotics. Annual Review of Control, Robotics, and Autonomous Systems, 5:254-277, 2022.
[10] H. Kurniawati, Y. Du, D. Hsu, and W. S. Lee. Motion planning under uncertainty for robotic tasks with long time horizons. In C. Pradalier, R. Siegwart, and G. Hirzinger, editors, Robotics Research, pages 151-168, Berlin, Heidelberg, 2011. Springer Berlin Heidelberg.
[11] H. Kurniawati, D. Hsu, and W. S. Lee. SARsOP: efficient point-based POMDP planning by approximating optimally reachable belief spaces. In C. S. W. Burgard, O. Brock, editor, Robotics: Science and Systems III, pages 65-72. MIT Press, 2008.
[12] H. Kurniawati and V. Yadav. An online POMDP solver for uncertainty planning in dynamic environment. In Robotics Research: The 16th International Symposium ISRR, pages 611-629, 2013.
[13] Z. Lim, L. Sun, and D. Hsu. Monte Carlo value iteration with macro-actions. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, editors, Advances in Neural Information Processing Systems, volume 24. Curran Associates, Inc., 2011.
[14] C. Lusena, J. Goldsmith, and M. Mundhenk. Nonapproximability results for partially observable Markov decision processes. Journal of Artificial Intelligence Research, 14:83-103, 2001.
[15] O. Madani, S. Hanks, and A. Condon. On the undecidability of probabilistic planning and related stochastic optimization problems. Artificial Intelligence, 147:5-34, 2003.
[16] G. E. Monahan. State of the art — a survey of partially observable Markov decision processes: theory, models, and algorithms. Manag. Sci., 28:1-16, 1982.
[17] C. H. Papadimitriou and J. N. Tsitsiklis. The complexity of Markov decision processes. Mathematics of Operations Research, 12(3):441-450, 1987.
[18] J. Pineau, G. Gordon, and T. S. Point-based value iteration: an anytime algorithm for POMDPs. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, volume 3, pages 1025-1032, 2003.
[19] 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.