Sidford, A., Wang, M., Wu, X., Yang, L., and Ye, Y. (2018). Near-optimal time and sample complexities for solving Markov decision processes with a generative model. In Advances in Neural Information Processing Systems, pages 5186-5196.
Smirnova, E., Dohmatob, E., and Mary, J. (2019). Distributionally robust reinforcement learning. arXiv preprint arXiv:1902.08708.
Sun, K., Liu, Y., Zhao, Y., Yao, H., Jui, S., and Kong, L. (2021). Exploring the training robustness of distributional reinforcement learning against noisy state observations. arXiv preprint arXiv:2109.08776.
Tamar, A., Mannor, S., and Xu, H. (2014). Scaling up robust MDPs using function approximation. In International conference on machine learning, pages 181-189. PMLR.
Tan, K. L., Esfandiari, Y., Lee, X. Y., and Sarkar, S. (2020). Robustifying reinforcement learning agents via action space adversarial training. In 2020 American control conference (ACC), pages 3959-3964. IEEE.
Tessler, C., Efroni, Y., and Mannor, S. (2019). Action robust reinforcement learning and applications in continuous control. In International Conference on Machine Learning, pages 6215-6224. PMLR.
Wainwright, M. J. (2019). Stochastic approximation with cone-contractive operators: Sharp  $\ell_{\infty}$ -bounds for Q-learning. arXiv preprint arXiv:1905.06265.
Wang, S., Si, N., Blanchet, J., and Zhou, Z. (2023a). A finite sample complexity bound for distributionally robust q-learning. arXiv preprint arXiv:2302.13203.
Wang, S., Si, N., Blanchet, J., and Zhou, Z. (2023b). On the foundation of distributionally robust reinforcement learning. arXiv preprint arXiv:2311.09018.
Wang, S., Si, N., Blanchet, J., and Zhou, Z. (2023c). Sample complexity of variance-reduced distributionally robust Q-learning. arXiv preprint arXiv:2305.18420.
Wang, Y. and Zou, S. (2021). Online robust reinforcement learning with model uncertainty. Advances in Neural Information Processing Systems, 34.
Wiesemann, W., Kuhn, D., and Rustem, B. (2013). Robust markov decision processes. Mathematics of Operations Research, 38(1):153-183.
Wolff, E. M., Topcu, U., and Murray, R. M. (2012). Robust control of uncertain markov decision processes with temporal logic specifications. In 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), pages 3372-3379. IEEE.
Woo, J., Joshi, G., and Chi, Y. (2023). The blessing of heterogeneity in federated Q-learning: Linear speedup and beyond. arXiv preprint arXiv:2305.10697.
Xie, T., Jiang, N., Wang, H., Xiong, C., and Bai, Y. (2021). Policy finetuning: Bridging sample-efficient offline and online reinforcement learning. Advances in neural information processing systems, 34.
Xiong, Z., Eappen, J., Zhu, H., and Jagannathan, S. (2022). Defending observation attacks in deep reinforcement learning via detection and denoising. arXiv preprint arXiv:2206.07188.
Xu, H. and Mannor, S. (2012). Distributionally robust Markov decision processes. Mathematics of Operations Research, 37(2):288-300.
Xu, Z., Panaganti, K., and Kalathil, D. (2023). Improved sample complexity bounds for distributionally robust reinforcement learning. arXiv preprint arXiv:2303.02783.
Yan, Y., Li, G., Chen, Y., and Fan, J. (2022). The efficacy of pessimism in asynchronous Q-learning. arXiv preprint arXiv:2203.07368.
Yang, K., Yang, L., and Du, S. (2021). Q-learning with logarithmic regret. In International Conference on Artificial Intelligence and Statistics, pages 1576-1584. PMLR.