Pieter Abbeel and Andrew Y Ng. Apprenticeship learning via inverse reinforcement learning. In Proceedings of the twenty-first international conference on Machine learning, page 1, 2004.
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel. Constrained policy optimization. In International conference on machine learning, pages 22-31. PMLR, 2017.
Eitan Altman. Constrained Markov decision processes, volume 7. CRC press, 1999.
Kareem Amin, Nan Jiang, and Satinder Singh. Repeated inverse reinforcement learning. Advances in neural information processing systems, 30, 2017.
Glen Chou, Dmitry Berenson, and Necmiye Ozay. Learning constraints from demonstrations. In *Algorithms Foundations of Robotics XIII: Proceedings of the 13th Workshop on the Algorithmic Foundations of Robotics* 13, pages 228-245. Springer, 2020.
Erwin Coumans and Yunfei Bai. Pybullet, a python module for physics simulation for games, robotics and machine learning. 2016.
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine. D4rl: Datasets for deep data-driven reinforcement learning. arXiv preprint arXiv:2004.07219, 2020.
Javier Garcia and Fernando Fernandez. A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research, 16(1):1437-1480, 2015.
Adam Gleave and Oliver Habryka. Multi-task maximum entropy inverse reinforcement learning. arXiv preprint arXiv:1805.08882, 2018.
Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, Yaodong Yang, and Alois Knoll. A review of safe reinforcement learning: Methods, theory and applications. arXiv preprint arXiv:2205.10330, 2022.
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al. Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905, 2018.
Dylan Hadfield-Menell, Smitha Milli, Pieter Abbeel, Stuart J Russell, and Anca Dragan. Inverse reward design. Advances in neural information processing systems, 30, 2017.
Jonathan Ho and Stefano Ermon. Generative adversarial imitation learning, 2016.
Dohyeong Kim and Songhwai Oh. Efficient off-policy safe reinforcement learning using trust region conditional value at risk. IEEE Robotics and Automation Letters, 7(3):7644-7651, 2022.
Kris M Kitani, Brian D Ziebart, James Andrew Bagnell, and Martial Hebert. Activity forecasting. In European conference on computer vision, pages 201-214. Springer, 2012.
J Zico Kolter, Mike P Rodgers, and Andrew Y Ng. A control architecture for quadruped locomotion over rough terrain. In 2008 IEEE International Conference on Robotics and Automation, pages 811-818. IEEE, 2008.
Qingkai Liang, Fanyu Que, and Eytan Modiano. Accelerated primal-dual policy optimization for safe reinforcement learning. arXiv preprint arXiv:1802.06480, 2018.
David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, and Andreas Krause. Learning safety constraints from demonstrations with unknown rewards. arXiv preprint arXiv:2305.16147, 2023.
Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Steven Wu, Bo Li, and Ding Zhao. Constrained variational policy optimization for safe reinforcement learning. In International Conference on Machine Learning, pages 13644-13668. PMLR, 2022.
Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li, and Ding Zhao. On the robustness of safe reinforcement learning under observational perturbations. In International Conference on Learning Representations, 2023.