[19] Will Dabney, Mark Rowland, Marc G. Bellemare, and Rémi Munos. Distributional reinforcement learning with quantile regression. In AAAI, 2018.
[20] Will Dabney, Georg Ostrovski, David Silver, and Rémi Munos. Implicit quantile networks for distributional reinforcement learning. In ICML, volume 80, pages 1104-1113, 2018.
[21] Jingliang Duan, Yang Guan, Shengbo Eben Li, Yangang Ren, Qi Sun, and Bo Cheng. Distributional soft actor-critic: Off-policy reinforcement learning for addressing value estimation errors. IEEE TNNLS, 2022.
[22] Wei-Fang Sun, Cheng-Kuang Lee, and Chun-Yi Lee. DFAC framework: Factorizing the value function via quantile mixture for multi-agent distributional q-learning. In ICML, 2021.
[23] Thomas J. Linsmeier and Neil D. Pearson. Value at risk. Financial Analysts Journal, 56(2):47-67, 2000.
[24] Xiaoteng Ma, Qiyuan Zhang, Li Xia, Zhengyuan Zhou, Jun Yang, and Qianchuan Zhao. Distributional soft actor critic for risk sensitive learning. ICML workshop, 2020.
[25] R.Tyrrell Rockafellar and Stanislav Uryasev. Conditional value-at-risk for general loss distributions. In Journal of Banking and Finance, 2002.
[26] Shaun S. Wang. A class of distortion operators for pricing financial and insurance risks. The Journal of Risk and Insurance, 67(1):15-36, 2000.
[27] Amos Tversky. Advances in Prospect Theory: Cumulative Representation of Uncertainty, pages 44-66. 09 2000.
[28] Jian Hu, Siyang Jiang, Seth Austin Harding, Haibin Wu, and Shih wei Liao. Rethinking the implementation tricks and monotonicity constraint in cooperative multi-agent reinforcement learning, 2022.
[29] Yaodong Yang, Jianye Hao, Ben Liao, Kun Shao, Guangyong Chen, Wulong Liu, and Hongyao Tang. Qatten: A general framework for cooperative multiagent reinforcement learning. CoRR, abs/2002.03939, 2020.
[30] Shariq Iqbal, Christian A. Schröder de Witt, Bei Peng, Wendelin Boehmer, Shimon Whiteson, and Fei Sha. Randomized entity-wise factorization for multi-agent reinforcement learning. In ICML, 2021.
[31] Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang, and Qiaomin Xie. Risk-sensitive reinforcement learning: Near-optimal risk-sample tradeoff in regret. In NeurIPS, 2020.
[32] Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima, Hirotaka Hachiya, and Toshiyuki Tanaka. Parametric return density estimation for reinforcement learning. In Peter Grünwald and Peter Spirtes, editors, UAI, pages 368-375. AUAI Press, 2010.
[33] Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori, Takashi Onishi, and Yoshimasa Tsuruoka. Learning robust options by conditional value at risk optimization. In NeurIPS, pages 2615-2625, 2019.
[34] Yingjie Fei, Zhuoran Yang, and Zhaoran Wang. Risk-sensitive reinforcement learning with function approximation: A debiasing approach. In ICML, pages 3198-3207, 2021.
[35] Núria Armengol Urpi, Sebastian Curi, and Andreas Krause. Risk-averse offline reinforcement learning. In ICLR. OpenReview.net, 2021.
[36] Shiau Hong Lim and Ilyas Malik. Distributional reinforcement learning for risk-sensitive policies. In NeurIPS, 2022.
[37] Jihwan Oh, Joonkee Kim, and Se-Young Yun. Risk perspective exploration in distributional reinforcement learning. NeurIPS workshop, abs/2206.14170, 2022.
[38] Sainbayar Sukhbaatar, Arthur Szlam, and Rob Fergus. Learning multiagent communication with backpropagation. In NeurIPS, 2016.