optimization. In Proceedings of the 37th International Conference on Machine Learning, volume 119, pages 11-22. PMLR, 2020.
[3] J. Achiam, D. Held, A. Tamar, and P. Abbeel. Constrained policy optimization. In Proceedings of the 34th International Conference on Machine Learning, volume 70, pages 22-31. PMLR, 2017.
[4] E. Altman. Constrained Markov Decision Processes. CRC Press, 1999.
[5] M. Andrychowicz, B. Baker, M. Chogiej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, and W. Zaremba. Learning dexterous in-hand manipulation. The International Journal of Robotics Research, 39(1):3-20, 2020. doi: 10.1177/0278364919887447.
[6] P. Artzner, F. Delbaen, J.-M. Eber, and D. Heath. Coherent measures of risk. Mathematical Finance, 9(3):203-228, 1999. doi: 10.1111/1467-9965.00068.
[7] Y. As, I. Usmanova, S. Curi, and A. Krause. Constrained policy optimization via Bayesian world models. In Tenth International Conference on Learning Representations, 2022.
[8] M. G. Bellemare, W. Dabney, and R. Munos. A distributional perspective on reinforcement learning. In Proceedings of the 34th International Conference on Machine Learning, volume 70, pages 449-458. PMLR, 2017.
[9] H. Bharadhwaj, A. Kumar, N. Rhinehart, S. Levine, F. Shkurti, and A. Garg. Conservative safety critics for exploration. In Ninth International Conference on Learning Representations, 2021.
[10] W. Cao, M. Benosman, and R. Ma. Domain knowledge-based automated analog circuit design with deep reinforcement learning. In The 59th ACM/IEEE Design Automation Conference, 2022.
[11] R. Chen and I. C. Paschalidis. Distributionally robust learning. Foundations and Trends® in Optimization, 4(1-2):1-243, 2020. doi: 10.1561/2400000026.
[12] Y. Chow, A. Tamar, S. Mannor, and M. Pavone. Risk-sensitive and robust decision-making: a CVaR optimization approach. In Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc., 2015.
[13] K. Chua, R. Calandra, R. McAllister, and S. Levine. Deep reinforcement learning in a handful of trials using probabilistic dynamics models. In Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018.
[14] W. Dabney, G. Ostrovski, D. Silver, and R. Munos. Implicit quantile networks for distributional reinforcement learning. In Proceedings of the 35th International Conference on Machine Learning, volume 80, pages 1096-1105. PMLR, 2018.
[15] E. Derman and S. Mannor. Distributional robustness and regularization in reinforcement learning. arXiv preprint, 2020. arXiv:2003.02894.
[16] E. Derman, D. J. Mankowitz, T. A. Mann, and S. Mannor. Soft-robust actor-critic policy-gradient. arXiv preprint, 2018. arXiv:1803.04848.
[17] J. Dhaene, A. Kukush, D. Linders, and Q. Tang. Remarks on quantiles and distortion risk measures. European Actuarial Journal, 2:319-328, 2012. doi: 10.1007/s13385-012-0058-0.
[18] G. Dulac-Arnold, N. Levine, D. J. Mankowitz, J. Li, C. Paduraru, S. Gowal, and T. Hester. An empirical investigation of the challenges of real-world reinforcement learning. arXiv preprint, 2020. arXiv:2003.11881.
[19] G. Dulac-Arnold, N. Levine, D. J. Mankowitz, J. Li, C. Paduraru, S. Gowal, and T. Hester. Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. Machine Learning, 110:2419-2468, 2021. doi: 10.1007/s10994-021-05961-4.