text: 166,195,1272702484096532481,1071113100739387392,Guanya Shi,"How valuable are predictions in online control? How many predictions are needed to achieve performance with O(1) dynamic regret? How well does MPC perform? We answer these in our new paper <LINK> Joint with Chenkai Yu, @yisongyue, Soon-Jo Chung and Adam Wierman. <LINK> We focus on online LQR control with stochastic and adversarial disturbances in dynamics. We characterize the cost-optimal and dynamic regret minimizing policies with k predictions of future disturbance, and show the marginal benefit of an extra prediction exponentially decays. We show that the greedy MPC is near-optimal - it only needs O(logT) predictions to reach O(1) dynamic regret (the same order as the required prediction horizon for O(1) regret). The power of predictions reduces the need for algorithmic sophistication due to the structure of LQR.",https://arxiv.org/abs/2006.07569,"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. ",The Power of Predictions in Online Control,3,"['How valuable are predictions in online control? How many predictions are needed to achieve performance with O(1) dynamic regret? How well does MPC perform? \nWe answer these in our new paper <LINK> Joint with Chenkai Yu, @yisongyue, Soon-Jo Chung and Adam Wierman. <LINK>', 'We focus on online LQR control with stochastic and adversarial disturbances in dynamics. We characterize the cost-optimal and dynamic regret minimizing policies with k predictions of future disturbance, and show the marginal benefit of an extra prediction exponentially decays.', 'We show that the greedy MPC is near-optimal - it only needs O(logT) predictions to reach O(1) dynamic regret (the same order as the required prediction horizon for O(1) regret). The power of predictions reduces the need for algorithmic sophistication due to the structure of LQR.']",20,06,827