[21] John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. Trust region policy optimization. In International conference on machine learning, pages 1889-1897. PMLR, 2015.
[22] Corrado Gini. Variabilità e mutabilità: contributo allo studio nelle distribuzioni e delle relazioni statistiche.[Fasc. I.]. Tipogr. di P. Cuppini, 1912.
[23] Shlomo Yitzhaki et al. Gini's mean difference: A superior measure of variability for non-normal distributions. *Metron*, 61(2):285-316, 2003.
[24] Gerald J Glasser. Variance formulas for the mean difference and coefficient of concentration. Journal of the American Statistical Association, 57(299):648-654, 1962.
[25] Edward Furman, Ruodu Wang, and Ricardas Zitikis. Gini-type measures of risk and variability: Gini shortfall, capital allocations, and heavy-tailed risks. Journal of Banking & Finance, 83:70-84, 2017.
[26] Gustave Choquet. Theory of capacities. In Annales de l'instit Fourier, volume 5, pages 131-295, 1954.
[27] Michel Grabisch. The application of fuzzy integrals in multicriteria decision making. European journal of operational research, 89(3):445-456, 1996.
[28] Ruodu Wang, Yunran Wei, and Gordon E Willmot. Characterization, robustness, and aggregation of signed choquet integrals. Mathematics of Operations Research, 45(3):993-1015, 2020.
[29] Shigeo Kusuoka. On law invariant coherent risk measures. In Advances in mathematical economics, pages 83-95. Springer, 2001.
[30] Bogdan Grechuk, Anton Molyboha, and Michael Zabarankin. Maximum entropy principle with general deviation measures. Mathematics of Operations Research, 34(2):445-467, 2009.
[31] R Tyrrell Rockafellar, Stan Uryasev, and Michael Zabarankin. Generalized deviations in risk analysis. Finance and Stochastics, 10(1):51-74, 2006.
[32] Fangda Liu, Jun Cai, Christiane Lemieux, and Ruodu Wang. Convex risk functionals: Representation and applications. Insurance: Mathematics and Economics, 90:66-79, 2020.
[33] Aviv Tamar, Yonatan Glassner, and Shie Mannor. Optimizing the cvar via sampling. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
[34] Will Dabney, Mark Rowland, Marc Bellemare, and Rémi Munos. Distributional reinforcement learning with quantile regression. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
[35] Will Dabney, Georg Ostrovski, David Silver, and Rémi Munos. Implicit quantile networks for distributional reinforcement learning. In International conference on machine learning, pages 1096-1105. PMLR, 2018.
[36] Fan Zhou, Zhoufan Zhu, Qi Kuang, and Liwen Zhang. Non-decreasing quantile function network with efficient exploration for distributional reinforcement learning. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 3455–3461, 8 2021.
[37] Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, and Pascal Poupart. Distributional reinforcement learning with monotonic splines. In International Conference on Learning Representations, 2022.
[38] Yuguang Yue, Zhendong Wang, and Mingyuan Zhou. Implicit distributional reinforcement learning. Advances in Neural Information Processing Systems, 33:7135-7147, 2020.
[39] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.