M Mehdi Afsar, Trafford Crump, and Behrouz Far. Reinforcement learning based recommender systems: A survey. ACM Computing Surveys, 55(7):1-38, 2022.
Arpit Agarwal, Rohan Ghuge, and Viswanath Nagarajan. Batched dueling bandits. In International Conference on Machine Learning, pages 89-110. PMLR, 2022.
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine learning, 47:235-256, 2002.
Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos. Minimax regret bounds for reinforcement learning. In International Conference on Machine Learning, pages 263-272. PMLR, 2017.
Yu Bai, Tengyang Xie, Nan Jiang, and Yu-Xiang Wang. Provably efficient q-learning with low switching cost. Advances in Neural Information Processing Systems, 32, 2019.
Nicolo Cesa-Bianchi, Ofer Dekel, and Ohad Shamir. Online learning with switching costs and other adaptive adversaries. Advances in Neural Information Processing Systems, 26, 2013.
Jinglin Chen and Nan Jiang. Information-theoretic considerations in batch reinforcement learning. In International Conference on Machine Learning, pages 1042-1051. PMLR, 2019.
Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, and Alekh Agarwal. On the statistical efficiency of reward-free exploration in non-linear rl. arXiv preprint arXiv:2206.10770, 2022.
Christoph Dann and Emma Brunskill. Sample complexity of episodic fixed-horizon reinforcement learning. In Advances in Neural Information Processing Systems (NIPS), 2015.
Christoph Dann, Tor Lattimore, and Emma Brunskill. Unifying pac and regret: Uniform pac bounds for episodic reinforcement learning. Advances in Neural Information Processing Systems, 30, 2017.
Christoph Dann, Lihong Li, Wei Wei, and Emma Brunskill. Policy certificates: Towards accountable reinforcement learning. In International Conference on Machine Learning, pages 1507-1516, 2019.
Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudik, and John Langford. Provably efficient rl with rich observations via latent state decoding. In International Conference on Machine Learning, pages 1665-1674. PMLR, 2019.
Yaqi Duan, Chi Jin, and Zhiyuan Li. Risk bounds and rademacher complexity in batch reinforcement learning. In International Conference on Machine Learning, pages 2892-2902. PMLR, 2021.
John Duchi, Feng Ruan, and Chulhee Yun. Minimax bounds on stochastic batched convex optimization. In Conference On Learning Theory, pages 3065-3162. PMLR, 2018.
Rick Durrett. Probability: theory and examples, volume 49. Cambridge university press, 2019.
Dylan J Foster, Akshay Krishnamurthy, David Simchi-Levi, and Yunzong Xu. Offline reinforcement learning: Fundamental barriers for value function approximation. arXiv preprint arXiv:2111.10919, 2021.
Minbo Gao, Tianle Xie, Simon S Du, and Lin F Yang. A provably efficient algorithm for linear markov decision process with low switching cost. arXiv preprint arXiv:2101.00494, 2021.
Zijun Gao, Yanjun Han, Zhimei Ren, and Zhengqing Zhou. Batched multi-armed bandits problem. Advances in Neural Information Processing Systems, 32, 2019.
Omer Gottesman, Fredrik Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li wei H. Lehman, Matthieu Komorowski, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, and Finale Doshi-Velez. Evaluating reinforcement learning algorithms in observational health settings, 2018.