[39] R. Tyrrell Rockafellar and Stanislav Uryasev. Optimization of conditional value-at-risk. Journal of Risk, 2:21-41, 2000.
[40] Reazul Hasan Russel and Marek Petrik. Beyond confidence regions: Tight Bayesian ambiguity sets for robust MDPs. In Proceedings of the 32nd Annual Conference on Neural Information Processing Systems (NeurIPS), 2019.
[41] Sergey Sarykalin, Gaia Serraino, and Stan Uryasev. Value-at-risk vs. conditional value-at-risk in risk management and optimization, chapter 13, pages 270-294. INFORMS, 2008.
[42] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. CoRR, abs/1707.06347, 2017.
[43] A. Shapiro, W. Tekaya, J. P. da Costa, and M. P. Soares. Risk-neutral and risk-averse stochastic dual dynamic programming method. European Journal of Operational Research, pages 375–391, 2013.
[44] Silvestr Stanko and Karel Macek. Risk-averse distributional reinforcement learning: A CVaR optimization approach. In International Joint Conference on Computational Intelligence, 2019.
[45] Lauren Steimle, David Kaufman, and Brian Denton. Multi-model Markov decision processes. Optimization Online, 2018.
[46] Alexander L. Strehl and Michael L. Littman. A theoretical analysis of model-based interval estimation. In International Conference on Machine Learning (ICML), 2005.
[47] Xihong Su and Marek Petrik. Solving multi-model MDPs by coordinate ascent and dynamic programming., In Uncertainty in Artificial Intelligence (UAI), 2023.
[48] Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An introduction. A Bradford Book, 2018.
[49] Aviv Tamar, Dotan Di Castro, and Shie Mannor. Policy gradients with variance related risk criteria. In Proceedings of the 29th International Coference on International Conference on Machine Learning, ICML'12, page 1651-1658, Madison, WI, USA, 2012. Omnipress. ISBN 9781450312851.
[50] Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, and Shie Mannor. Policy gradient for coherent risk measures. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc., 2015.
[51] A. W. van der Vaart. Asymptotic statistics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, 1998.
[52] Nithia Vijayan and L. A. Prashanth. A policy gradient approach for optimization of smooth risk measures. In Robin J. Evans and Ilya Shpitser, editors, Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, volume 216 of Proceedings of Machine Learning Research, pages 2168-2178. PMLR, 31 Jul-04 Aug 2023.
[53] Wolfram Wiesemann, Daniel Kuhn, and Berç Rustem. Robust Markov decision processes. Mathematics of Operations Research, 38(1):153-183, 2013.
[54] Huan Xu and Shie Mannor. The robustness-performance trade-off in Markov decision processes. In Proceedings of the 19th Annual Conference on Neural Information Processing Systems (NeurIPS), 2006.
[55] Huan Xu and Shie Mannor. Distributionally robust Markov decision processes. Mathematics of Operations Research, 37(2):288-300, 2012.
[56] Paul Herbert Zipkin. Foundations of inventory management. McGraw-Hill, Boston, 2000.