[19] N. Golowich, A. Moitra, and D. Rohatgi. Learning in observable pomdps, without computationally intractable oracles. In Advances in Neural Information Processing Systems, 2022.
[20] A. Huang, J. Chen, and N. Jiang. Reinforcement learning in low-rank mdps with density features. International Conference on Machine Learning (ICML), 2023.
[21] N. Jiang, A. Krishnamurthy, A. Agarwal, J. Langford, and R. E. Schapire. Contextual decision processes with low Bellman rank are PAC-learnable. In International Conference on Machine Learning, pages 1704–1713, 2017.
[22] C. Jin, P. Netrapalli, R. Ge, S. M. Kakade, and M. I. Jordan. A short note on concentration inequalities for random vectors with subgaussian norm. arXiv preprint arXiv:1902.03736, 2019.
[23] C. Jin, Z. Yang, Z. Wang, and M. I. Jordan. Provably efficient reinforcement learning with linear function approximation. In Conference on Learning Theory, pages 2137-2143, 2020.
[24] C. Jin, Q. Liu, and S. Miryoosefi. Bellman eluder dimension: New rich classes of RL problems, and sample-efficient algorithms. arXiv preprint arXiv:2102.00815, 2021.
[25] S. M. Kakade. On the sample complexity of reinforcement learning. University of London, University College London (United Kingdom), 2003.
[26] J. Kober, J. A. Bagnell, and J. Peters. Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, 32(11):1238-1274, 2013.
[27] J. Li, W. Monroe, A. Ritter, D. Jurafsky, M. Galley, and J. Gao. Deep reinforcement learning for dialogue generation. In EMNLP, 2016.
[28] T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.
[29] Z. Mhammedi, D. J. Foster, M. Simchowitz, D. Misra, W. Sun, A. Krishnamurthy, A. Rakhlin, and J. Langford. Learning the linear quadratic regulator from nonlinear observations. Advances in Neural Information Processing Systems, 33:14532-14543, 2020.
[30] Z. Mhammedi, A. Block, D. J. Foster, and A. Rakhlin. Efficient model-free exploration in low-rank mdps. arXiv preprint arXiv:2307.03997, 2023.
[31] Z. Mhammedi, D. J. Foster, and A. Rakhlin. Representation learning with multi-step inverse kinematics: An efficient and optimal approach to rich-observation rl. International Conference on Machine Learning (ICML), 2023.
[32] D. Misra, M. Henaff, A. Krishnamurthy, and J. Langford. Kinematic state abstraction and provably efficient rich-observation reinforcement learning. arXiv preprint arXiv:1911.05815, 2019.
[33] D. Misra, M. Henaff, A. Krishnamurthy, and J. Langford. Kinematic state abstraction and provably efficient rich-observation reinforcement learning. In International conference on machine learning, pages 6961–6971. PMLR, 2020.
[34] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529, 2015.
[35] A. Modi, J. Chen, A. Krishnamurthy, N. Jiang, and A. Agarwal. Model-free representation learning and exploration in low-rank mdps. CoRR, abs/2102.07035, 2021.
[36] S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, April 26-30, 2010, pages 811-820. ACM, 2010.
[37] D. Russo and B. Van Roy. Eluder dimension and the sample complexity of optimistic exploration. In Advances in Neural Information Processing Systems, pages 2256-2264, 2013.
[38] D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):484, 2016.
[39] W. Sun, N. Jiang, A. Krishnamurthy, A. Agarwal, and J. Langford. Model-based RL in contextual decision processes: PAC bounds and exponential improvements over model-free approaches. In Conference on learning theory, pages 2898-2933. PMLR, 2019.