In order to certify performance and safety, feedback control requires precise characterization of sensor errors. In this paper, we provide guarantees on such feedback systems when sensors are characterized by solving a supervised learning problem. We show a uniform error bound on nonparametric kernel regression under a dynamically-achievable dense sampling scheme. This allows for a finite-time convergence rate on the sub-optimality of using the regressor in closed-loop for waypoint tracking. We demonstrate our results in simulation with simplified unmanned aerial vehicle and autonomous driving examples.
Beyond Non-Degeneracy: Revisiting Certainty Equivalent Heuristic for Online Linear Programming
School of Data Science, the Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Data Science and Analytics Thrust, Information Hub, the Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息枢纽数据科学与分析学域）
Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents
City University of Hong Kong（香港城市大学）；University of Chicago（芝加哥大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shanghai Jiao Tong University（上海交通大学）
