2020/06/13 by Chenkai Yu, Guanya Shi, Yu, Chenkai +7 · 1 citation
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Dynamical Systems (math.DS) #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Optimization and Search Problems #Reinforcement Learning in Robotics #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2006.07569
openalex publication_date 2020/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the impact of predictions in online Linear Quadratic Regulator control with both stochastic and adversarial disturbances in the dynamics. In both settings, we characterize the optimal policy and derive tight bounds on the minimum cost and dynamic regret. Perhaps surprisingly, our analysis shows that the conventional greedy MPC approach is a near-optimal policy in both stochastic and adversarial settings. Specifically, for length-T problems, MPC requires only O(log T) predictions to reach O(1) dynamic regret, which matches (up to lower-order terms) our lower bound on the required prediction horizon for constant regret.