Krishna C Kalagarla, Rahul Jain, and Pierluigi Nuzzo. A sample-efficient algorithm for episodic finite-horizon mdp with constraints. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pp. 8030-8037, 2021.
Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, and Michal Valko. Adaptive reward-free exploration. In Algorithmic Learning Theory, pp. 865-891. PMLR, 2021.
Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau, Vinicius Zambaldi, Satyaki Upadhyay, Julien Pérolat, Sriram Srinivasan, Finbarr Timbers, Karl Tuyls, Shayegan Omidshafiei, et al. Openspiel: A framework for reinforcement learning in games. arXiv preprint arXiv:1908.09453, 2019.
Hoang Le, Cameron Voloshin, and Yisong Yue. Batch policy learning under constraints. In International Conference on Machine Learning, pp. 3703-3712. PMLR, 2019.
Tao Liu, Ruida Zhou, Dileep Kalathil, Panganamala Kumar, and Chao Tian. Learning policies with zero or bounded constraint violation for constrained mdps. Advances in Neural Information Processing Systems, 34:17183-17193, 2021a.
Tao Liu, Ruida Zhou, Dileep Kalathil, Panganamala Kumar, and Chao Tian. Learning policies with zero or bounded constraint violation for constrained mdps. Advances in Neural Information Processing Systems, 34:17183-17193, 2021b.
Yongshuai Liu, Jiaxin Ding, and Xin Liu. Ipo: Interior-point policy optimization under constraints. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pp. 4940-4947, 2020.
Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, and Michal Valko. Fast active learning for pure exploration in reinforcement learning. In International Conference on Machine Learning, pp. 7599-7608. PMLR, 2021.
Sobhan Miryosefi and Chi Jin. A simple reward-free approach to constrained reinforcement learning. In International Conference on Machine Learning, pp. 15666-15698. PMLR, 2022.
Ian Osband and Benjamin Van Roy. On lower bounds for regret in reinforcement learning. arXiv preprint arXiv:1608.02732, 2016.
Shuang Qiu, Xiaohan Wei, Zhuoran Yang, Jieping Ye, and Zhaoran Wang. Upper confidence primal-dual reinforcement learning for cmdp with adversarial loss. Advances in Neural Information Processing Systems, 33:15277-15287, 2020.
Ming Shi, Yingbin Liang, and Ness Shroff. A near-optimal algorithm for safe reinforcement learning under instantaneous hard constraints. arXiv preprint arXiv:2302.04375, 2023.
Thiago D Simão, Nils Jansen, and Matthijs TJ Span. Always safe: Reinforcement learning without safety constraint violations during training. 2021.
Rahul Singh, Abhishek Gupta, and Ness B Shroff. Learning in markov decision processes under constraints. arXiv preprint arXiv:2002.12435, 2020.
Aivar Sootla, Alexander Cowen-Rivers, Jun Wang, and Haitham Bou Ammar. Enhancing safe exploration using safety state augmentation. Advances in Neural Information Processing Systems, 35:34464-34477, 2022.
Adam Stooke, Joshua Achiam, and Pieter Abbeel. Responsive safety in reinforcement learning by pid lagrangian methods. In International Conference on Machine Learning, pp. 9133-9143. PMLR, 2020.
Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
Chen Tessler, Daniel J Mankowitz, and Shie Mannor. Reward constrained policy optimization. arXiv preprint arXiv:1805.11074, 2018.
Garrett Thomas, Yuping Luo, and Tengyu Ma. Safe reinforcement learning by imagining the near future. Advances in Neural Information Processing Systems, 34:13859-13869, 2021.