[21] Alberto Maria Metelli, Filippo Lazzati, and Marcello Restelli. Towards theoretical understanding of inverse reinforcement learning. arXiv preprint arXiv:2304.12966, 2023.
[22] Sean P Meyn and Richard L Tweedie. Markov chains and stochastic stability. Springer Science & Business Media, 2012.
[23] Tianwei Ni, Harshit Sikchi, Yufei Wang, Tejus Gupta, Lisa Lee, and Ben Eysenbach. f-irl: Inverse reinforcement learning via state marginal matching. In Conference on Robot Learning, pages 529–551. PMLR, 2021.
[24] G Ch Pflug. On-line optimization of simulated markovian processes. Mathematics of Operations Research, 15(3):381-395, 1990.
[25] Georg Ch Pflug. Derivatives of probability measures-concepts and applications to the optimization of stochastic systems. In Discrete Event Systems: Models and Applications: IIASA Conference Sopron, Hungary, August 3-7, 1987, pages 252-274. Springer, 2006.
[26] Robert Pinsler, Max Maag, Oleg Arenz, and Gerhard Neumann. Inverse reinforcement learning of bird flocking behavior. In ICRA Swarms Workshop, 2018.
[27] Martin L Puterman. Markov decision processes: Discrete stochastic dynamic programming. John Wiley & Sons, 2014.
[28] Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann. Stable-baselines3: Reliable reinforcement learning implementations. Journal of Machine Learning Research, 22(268):1-8, 2021.
[29] Stefan Schaal. Is imitation learning the route to humanoid robots? Trends in cognitive sciences, 3(6):233-242, 1999.
[30] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
[31] Can Eren Sezener, Eiji Uchibe, and Kenji Doya. Obtaining reward functions of rats using inverse reinforcement learning. 2014.
[32] Ziyu Wang, Josh S Merel, Scott E Reed, Nando de Freitas, Gregory Wayne, and Nicolas Heess. Robust imitation of diverse behaviors. Advances in Neural Information Processing Systems, 30, 2017.
[33] Tengyu Xu, Zhe Wang, and Yingbin Liang. Improving sample complexity bounds for (natural) actor-critic algorithms. Advances in Neural Information Processing Systems, 33:4358-4369, 2020.
[34] Shoichiro Yamaguchi, Honda Naoki, Muneki Ikeda, Yuki Tsukada, Shunji Nakano, Ikue Mori, and Shin Ishii. Identification of animal behavioral strategies by inverse reinforcement learning. PLoS computational biology, 14(5):e1006122, 2018.
[35] Tom Zahavy, Alon Cohen, Haim Kaplan, and Yishay Mansour. Average reward reinforcement learning with unknown mixing times. In *The Conference on Uncertainty in Artificial Intelligence (UAI)*, 2020.
[36] Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, and Satinder Singh. Reward is enough for convex mdps. Advances in Neural Information Processing Systems, 34:25746-25759, 2021.
[37] Siliang Zeng, Chenliang Li, Alfredo Garcia, and Mingyi Hong. Maximum-likelihood inverse reinforcement learning with finite-time guarantees. arXiv preprint arXiv:2210.01808, 2022.
[38] Siliang Zeng, Chenliang Li, Alfredo Garcia, and Mingyi Hong. Understanding expertise through demonstrations: A maximum likelihood framework for offline inverse reinforcement learning. arXiv preprint arXiv:2302.07457, 2023.