Chi Jin, Sham Kakade, Akshay Krishnamurthy, and Qinghua Liu. Sample-efficient reinforcement learning of undercomplete POMDPs. Advances in Neural Information Processing Systems, 33: 18530-18539, 2020.
Pooria Joulani, Andras Gyorgy, and Csaba Szepesvári. Online learning under delayed feedback. In Proceedings of the International Conference on Machine Learning, pages 1453-1461. PMLR, 2013.
Konstantinos V Katsikopoulos and Sascha E Engelbrecht. Markov decision processes with delays and asynchronous cost collection. IEEE Transactions on Automatic Control, 48(4):568-574, 2003.
Michael Kearns and Satinder Singh. Near-optimal reinforcement learning in polynomial time. Machine Learning, 49(2):209-232, 2002.
Vikram Krishnamurthy. *Partially Observed Markov Decision Processes*. Cambridge University Press, 2016.
Tal Lancewicki, Shahar Segal, Tomer Koren, and Yishay Mansour. Stochastic multi-armed bandits with unrestricted delay distributions. In Proceedings of the International Conference on Machine Learning, pages 5969-5978. PMLR, 2021.
Tor Lattimore and Csaba Szepesvári. Bandit Algorithms. Cambridge University Press, 2020.
Jonathan N Lee, Alekh Agarwal, Christoph Dann, and Tong Zhang. Learning in pomdps is sample-efficient with hindsight observability. arXiv preprint arXiv:2301.13857, 2023.
Roderick J Little, Ralph D'Agostino, Michael L Cohen, Kay Dickersin, Scott S Emerson, John T Farrar, Constantine Frangakis, Joseph W Hogan, Geert Molenberghs, Susan A Murphy, et al. The prevention and treatment of missing data in clinical trials. New England Journal of Medicine, 367 (14):1355-1360, 2012.
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvari, and Chi Jin. Optimistic MLE-A generic model-based algorithm for partially observable sequential decision making. arXiv preprint arXiv:2209.14997, 2022.
Shichao Liu, Xiaoyu Wang, and Peter Xiaoping Liu. Impact of communication delays on secondary frequency control in an islanded microgrid. IEEE Transactions on Industrial Electronics, 62(4): 2021-2031, 2014.
Daniel J Lizotte, Lacey Gunter, Eric Laber, and Susan A Murphy. Missing data and uncertainty in batch reinforcement learning. In Advances in Neural Information Processing Systems, 2008.
Christos H Papadimitriou and John N Tsitsiklis. The complexity of markov decision processes. Mathematics of Operations Research, 12(3):441-450, 1987.
Pascal Poupart and Nikos Vlassis. Model-based bayesian reinforcement learning in partially observable domains. In Proceedings of the International Symposium on Artificial Intelligence and Mathematics, pages 1-2, 2008.
Stephane Ross, Brahim Chaib-draa, and Joelle Pineau. Bayes-adaptive pomdps. Advances in Neural Information Processing Systems, 20, 2007.
Richard D Smallwood and Edward J Sondik. The optimal control of partially observable markov processes over a finite horizon. Operations Research, 21(5):1071-1088, 1973.
Richard S Sutton and Andrew G Barto. Reinforcement Learning: An Introduction. MIT Press, 2018.
Masatoshi Uehara, Ayush Sekhari, Jason D Lee, Nathan Kallus, and Wen Sun. Provably efficient reinforcement learning in partially observable dynamical systems. arXiv preprint arXiv:2206.12020, 2022.
Claire Vernade, Olivier Cappé, and Vianney Perchet. Stochastic bandit models for delayed conversions. arXiv preprint arXiv:1706.09186, 2017.