[1] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
[2] Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
[3] Yunke Wang, Chang Xu, Bo Du, and Honglak Lee. Learning to weight imperfect demonstrations. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 10961-10970. PMLR, 2021.
[4] Yunke Wang, Chang Xu, and Bo Du. Robust adversarial imitation learning via adaptively-selected demonstrations. In Zhi-Hua Zhou, editor, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, pages 3155-3161. ijcai.org, 2021.
[5] Diederik M Roijers, Peter Vamplew, Shimon Whiteson, and Richard Dazeley. A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48:67-113, 2013.
[6] Conor F. Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Irissappane, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, and Diederik M. Roijers. A practical guide to multi-objective reinforcement learning and planning. Auton. Agents Multi Agent Syst., 36(1):26, 2022.
[7] Lucas Nunes Alegre, Ana L. C. Bazzan, Diederik M. Roijers, Ann Nowé, and Bruno C. da Silva. Sample-efficient multi-objective learning via generalized policy improvement prioritization. CoRR, abs/2301.07784, 2023.
[8] Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, and Wojciech Matusik. Prediction-guided multi-objective reinforcement learning for continuous robot control. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 10607-10616. PMLR, 2020.
[9] Anirban Majumdar and Claire J Tomlin. Risk-sensitive sequential decision making: Uncertainty-based performance metrics and risk-sensitive control. Foundations and Trends® in Systems and Control, 4(4):265-365, 2017.
[10] Shiau Hong Lim and Ilyas Malik. Distributional reinforcement learning for risk-sensitive policies. In Advances in Neural Information Processing Systems, 2022.
[11] Aivar Sootla, Alexander I Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David H Mguni, Jun Wang, and Haitham Ammar. Saute rl: Almost surely safe reinforcement learning using state augmentation. In International Conference on Machine Learning, pages 20423-20443. PMLR, 2022.
[12] Mathieu Reymond, Conor F Hayes, Denis Steckelmacher, Diederik M Roijers, and Ann Nowé. Actor-critic multi-objective reinforcement learning for non-linear utility functions. Autonomous Agents and Multi-Agent Systems, 37(2):23, 2023.
[13] Haim Levy. Stochastic dominance and expected utility: Survey and analysis. Management science, 38(4):555-593, 1992.
[14] Moshe Shaked and J George Shanthikumar. Stochastic orders. Springer Science Business Media, 2007.
[15] Patrick Mannion, Jim Duggan, and Enda Howley. Dynamic treatment regimes in mobile health. In 2016 International Joint Conference on Neural Networks (IJCNN), pages 526-533. IEEE, 2016.