Aristide C. Y. Tossou, Debabrota Basu, and Christos Dimitrakakis. Near-optimal optimistic reinforcement learning using empirical bernstein inequalities. CoRR, abs/1905.12425, 2019.
Masatoshi Uehara, Xuezhou Zhang, and Wen Sun. Representation learning for online and offline RL in low-rank mdps. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022.
Ruosong Wang, Ruslan Salakhutdinov, and Lin F. Yang. Reinforcement learning with general value function approximation: Provably efficient approach via bounded eluder dimension. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.
Ruosong Wang, Dean P. Foster, and Sham M. Kakade. What are the statistical limits of offline RL with linear function approximation? In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021a.
Yining Wang, Ruosong Wang, Simon Shaolei Du, and Akshay Krishnamurthy. Optimism in reinforcement learning with generalized linear function approximation. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021b.
Gellert Weisz, Philip Amortila, and Csaba Szepesvári. Exponential lower bounds for planning in mdps with linearly-realizable optimal action-value functions. In Algorithmic Learning Theory, 16-19 March 2021, Virtual Conference, Worldwide, volume 132 of Proceedings of Machine Learning Research, pages 1237-1264. PMLR, 2021.
Yue Wu, Dongruo Zhou, and Quanquan Gu. Nearly minimax optimal regret for learning infinite-horizon average-reward mdps with linear function approximation. In International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28-30 March 2022, Virtual Event, volume 151 of Proceedings of Machine Learning Research, pages 3883-3913. PMLR, 2022.
Zhihui Xie, Zichuan Lin, Junyou Li, Shuai Li, and Deheng Ye. Pretraining in deep reinforcement learning: A survey. CoRR, abs/2211.03959, 2022.
Lin Yang and Mengdi Wang. Sample-optimal parametric q-learning using linearly additive features. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 6995-7004. PMLR, 2019.
Mengjiao Yang and Ofir Nachum. Representation matters: Offline pretraining for sequential decision making. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 11784-11794. PMLR, 2021.
Jia Yuan Yu, Shie Mannor, and Nahum Shimkin. Markov decision processes with arbitrary reward processes. Math. Oper. Res., 34(3):737-757, 2009.
Andrea Zanette, David Brandfonbrener, Emma Brunskill, Matteo Pirotta, and Alessandro Lazaric. Frequentist regret bounds for randomized least-squares value iteration. In The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy], volume 108 of Proceedings of Machine Learning Research, pages 1954-1964. PMLR, 2020.
Andrea Zanette, Ching-An Cheng, and Alekh Agarwal. Cautiously optimistic policy optimization and exploration with linear function approximation. In *Conference on Learning Theory*, COLT 2021, 15-19 August 2021, Boulder, Colorado, USA, volume 134 of *Proceedings of Machine Learning Research*, pages 4473-4525. PMLR, 2021.
Tong Zhang. From  $\varepsilon$ -entropy to kl-entropy: Analysis of minimum information complexity density estimation. The Annals of Statistics, 34(5):2180-2210, 2006.