Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
This paper considers online optimal control with linear constraints on the states and actions under a noisy linear dynamical system. The convex stage cost functions are adversarially changing and are unknown before selecting the stage actions. The dynamical system and the constraints are time-invariant and known beforehand. We propose an online control algorithm: Online Gradient Descent with Buffer Zone (OGD-BZ). OGD-BZ can guarantee the system to satisfy all the constraints despite the random process noises. We investigate the policy regret of OGD-BZ, which refers to the difference between OGD-BZ's total cost and the total cost of an optimal linear policy in hindsight. We show that OGD-BZ achieves regret, where absorbs logarithmic terms of .
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Natalia Martinez Gil, Dhaval Patel, et al.
UAI 2024
Shubhi Asthana, Pawan Chowdhary, et al.
KDD 2021
Baifeng Shi, Judy Hoffman, et al.
NeurIPS 2020