George Nemhauser and Laurence Wolsey. Polynomial-time algorithms for linear programming. Integer and Combinatorial Optimization, pages 146-181, 1988.
Dung Daniel Ngo, Giuseppe Vietri, and Zhiwei Steven Wu. Improved regret for differentially private exploration in linear mdp. arXiv preprint arXiv:2202.01292, 2022.
Hajime Ono and Tsubasa Takahashi. Locally private distributed reinforcement learning. arXiv preprint arXiv:2001.11718, 2020.
Dan Qiao and Yu-Xiang Wang. Near-optimal differentially private reinforcement learning. In International Conference on Artificial Intelligence and Statistics, pages 9914-9940. PMLR, 2023a.
Dan Qiao and Yu-Xiang Wang. Near-optimal deployment efficiency in reward-free reinforcement learning with linear function approximation. In The Eleventh International Conference on Learning Representations, 2023b.
Dan Qiao, Ming Yin, Ming Min, and Yu-Xiang Wang. Sample-efficient reinforcement learning with loglog(T) switching cost. In Proceedings of the 39th International Conference on Machine Learning, pages 18031-18061. PMLR, 2022.
Dan Qiao, Ming Yin, and Yu-Xiang Wang. Logarithmic switching cost in reinforcement learning beyond linear mdps. arXiv preprint arXiv:2302.12456, 2023.
Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi, Peter Szolovits, and Marzyeh Ghassemi. Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach. In Machine Learning for Healthcare Conference, pages 147-163, 2017.
Rachel Redberg and Yu-Xiang Wang. Privately publishable per-instance privacy. Advances in Neural Information Processing Systems, 34, 2021.
Ahmad EL Sallab, Mohammed Abdou, Etienne Perot, and Senthil Yogamani. Deep reinforcement learning framework for autonomous driving. *Electronic Imaging*, 2017(19):70-76, 2017.
Roshan Shariff and Or Sheffet. Differentially private contextual linear bandits. Advances in Neural Information Processing Systems, 31, 2018.
Karthik Sridharan. A gentle introduction to concentration inequalities. Dept. Comput. Sci., Cornell Univ., Tech. Rep, 2002.
Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
Aristide Charles Yedia Tossou and Christos Dimitrakakis. Achieving privacy in the adversarial multi-armed bandit. In Thirty-First AAAI Conference on Artificial Intelligence, 2017.
Tim Van Erven and Peter Harremos. Rényi divergence and kullback-leibler divergence. IEEE Transactions on Information Theory, 60(7):3797-3820, 2014.
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, and Steven Wu. Private reinforcement learning with pac and regret guarantees. In International Conference on Machine Learning, pages 9754-9764. PMLR, 2020.
Baoxiang Wang and Nidhi Hegde. Privacy-preserving q-learning with functional noise in continuous spaces. Advances in Neural Information Processing Systems, 32, 2019.
Ruosong Wang, Dean P Foster, and Sham M Kakade. What are the statistical limits of offline rl with linear function approximation? International Conference on Learning Representations, 2021a.
Tianhao Wang, Dongruo Zhou, and Quanquan Gu. Provably efficient reinforcement learning with linear function approximation under adaptivity constraints. Advances in Neural Information Processing Systems, 34:13524-13536, 2021b.
Tengyang Xie, Philip S Thomas, and Gerome Miklau. Privacy preserving off-policy evaluation. arXiv preprint arXiv:1902.00174, 2019.