[9] Weichao Mao, Kaiqing Zhang, Ruihao Zhu, David Simchi-Levi, and Tamer Basar. Near-optimal model-free reinforcement learning in non-stationary episodic mdps. In International Conference on Machine Learning, pages 7447-7458. PMLR, 2021.
[10] Yuwei Luo, Varun Gupta, and Mladen Kolar. Dynamic regret minimization for control of nonstationary linear dynamical systems. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 6(1):1-72, 2022.
[11] Peng Zhao, Long-Fei Li, and Zhi-Hua Zhou. Dynamic regret of online markov decision processes. In International Conference on Machine Learning, pages 26865–26894. PMLR, 2022.
[12] Christian Scheller, Yanick Schraner, and Manfred Vogel. Sample efficient reinforcement learning through learning from demonstrations in mycraft. In NeurIPS 2019 Competition and Demonstration Track, pages 67-76. PMLR, 2020.
[13] Matthew Botvinick, Sam Ritter, Jane X Wang, Zeb Kurth-Nelson, Charles Blundell, and Denis Hassabis. Reinforcement learning, fast and slow. Trends in cognitive sciences, 23(5):408-422, 2019.
[14] Xueying Bai, Jian Guan, and Hongning Wang. A model-based reinforcement learning with adversarial training for online recommendation. Advances in Neural Information Processing Systems, 32, 2019.
[15] Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, and Adam Wierman. Online optimization with memory and competitive control. Advances in Neural Information Processing Systems, 33:20636-20647, 2020.
[16] Gautam Goel and Babak Hassibi. Competitive control. IEEE Transactions on Automatic Control, 2022.
[17] Tongxin Li, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, and Steven Low. Robustness and consistency in linear quadratic control with untrusted predictions. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 6(1):1-35, 2022.
[18] Oron Sabag, Sahin Lale, and Babak Hassibi. Optimal competitive-ratio control. arXiv preprint arXiv:2206.01782, 2022.
[19] Yiheng Lin, Yang Hu, Guannan Qu, Tongxin Li, and Adam Wierman. Bounded-regret mpc via perturbation analysis: Prediction error, constraints, and nonlinearity. Advances in Neural Information Processing Systems, 35:36174-36187, 2022.
[20] Tongxin Li, Yue Chen, Bo Sun, Adam Wierman, and Steven H Low. Information aggregation for constrained online control. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 5(2):1-35, 2021.
[21] Tongxin Li, Bo Sun, Yue Chen, Zixin Ye, Steven H Low, and Adam Wierman. Learning-based predictive control via real-time aggregate flexibility. IEEE Transactions on Smart Grid, 12(6):4897-4913, 2021.
[22] Alexander Wei and Fred Zhang. Optimal robustness-consistency trade-offs for learning-augmented online algorithms. Advances in Neural Information Processing Systems, 33:8042-8053, 2020.
[23] Shom Banerjee. Improving online rent-or-buy algorithms with sequential decision making and ml predictions. Advances in Neural Information Processing Systems, 33:21072-21080, 2020.
[24] Stephen Tu, Alexander Robey, Tingnan Zhang, and Nikolai Matni. On the sample complexity of stability constrained imitation learning. In Learning for Dynamics and Control Conference, pages 180-191. PMLR, 2022.
[25] Hiroyasu Tsukamoto, Soon-Jo Chung, and Jean-Jaques E Slotine. Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview. Annual Reviews in Control, 52:135-169, 2021.