[15] M. Hoerger and H. Kurniawati. An on-line POMDP solver for continuous observation spaces. In International Conference on Robotics and Automation (ICRA), pages 7643-7649. IEEE, 2021.
[16] S. Katt, F. A. Oliehoek, and C. Amato. Learning in POMDPs with Monte Carlo Tree Search. In International Conference on Machine Learning (ICML), pages 1819-1827, 2017.
[17] S. Katt, F. A. Oliehoek, and C. Amato. Bayesian Reinforcement Learning in Factored POMDPs. In International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), 2019.
[18] P. Kozakowski, M. Pacek, and P. Milos. Planning and learning using adaptive entropy tree search. In International Joint Conference on Neural Networks (IJCNN). IEEE, 2022.
[19] H. Kurniawati, D. Hsu, and W. S. Lee. SARsOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces. In Robotics: Science and systems, 2008.
[20] T. Lai and H. Robbins. Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics, 6(1):4-22, 1985. ISSN 0196-8858.
[21] M. Lauri and R. Ritala. Planning for robotic exploration based on forward simulation. Robotics and Autonomous Systems, 83:15-31, 2016.
[22] L. S. Marcolino and L. Chaimowicz. No robot left behind: Coordination to overcome local minima in swarm navigation. In International Conference on Robotics and Automation (ICRA), pages 1904-1909. IEEE, 2008.
[23] G. Mazzi, D. Meli, A. Castellini, and A. Farinelli. Learning logic specifications for soft policy guidance in POMCP. arXiv preprint arXiv:2303.09172, 2023.
[24] J. Mern, A. Yildiz, Z. Sunberg, T. Mukerji, and M. J. Kochenderfer. Bayesian optimized Monte Carlo planning. In AAAI Conference on Artificial Intelligence, volume 35. No. 13, pages 11880-11887, 2021.
[25] R. O. Moraes, J. R. Marino, L. H. Lelis, and M. A. Nascimento. Action abstractions for combinatorial multi-armed bandit tree search. In Artificial Intelligence and Interactive Digital Entertainment Conference, 2018.
[26] L. Niss and A. Tewari. What you see may not be what you get: UCB bandit algorithms robust to  $\varepsilon$ -contamination. In Conference on Uncertainty in Artificial Intelligence, pages 450-459. PMLR, 2020.
[27] G. Papoudakis, F. Christianos, and S. Albrecht. Agent modelling under partial observability for deep reinforcement learning. Advances in Neural Information Processing Systems, 34, 2021.
[28] L. Pelcner, S. Li, M. Aparecido do Carmo Alves, L. Soriano Marcolino, and A. Collins. Real-time learning and planning in environments with swarms: A hierarchical and a parameter-based simulation approach. In International Conference on Autonomous Agents and MultiAgent Systems (AAMAS), page 1019-1027, 2020.
[29] F. Riccio, R. Capobianco, and D. Nardi. Loop: Iterative learning for optimistic planning on robots. Robotics and Autonomous Systems, 136:103693, 2021.
[30] E. Shafipour Yourdshahi, M. A. do Carmo Alves, A. Varma, L. Soriano Marcolino, J. Ueyama, and P. Angelov. On-line estimators for ad-hoc task execution: learning types and parameters of teammates for effective teamwork. Autonomous Agents and Multi-Agent Systems, 36(2):45, 2022.
[31] D. Shah, Q. Xie, and Z. Xu. Non-asymptotic analysis of Monte Carlo Tree Search. In Abstracts of the SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems, pages 31-32, 2020.
[32] D. Silver and J. Veness. Monte-carlo planning in large POMDPs. In Neural Information Processing Systems, pages 2164--2172, 2010.