Adaptive Regret for Control of Time-Varying Dynamics

07/08/2020
by   Paula Gradu, et al.
0

We consider regret minimization for online control with time-varying linear dynamical systems. The metric of performance we study is adaptive policy regret, or regret compared to the best policy on any interval in time. We give an efficient algorithm that attains first-order adaptive regret guarantees for the setting of online convex optimization with memory. We also show that these first-order bounds are nearly tight. This algorithm is then used to derive a controller with adaptive regret guarantees that provably competes with the best linear controller on any interval in time. We validate these theoretical findings experimentally on simulations of time-varying dynamics and disturbances.

READ FULL TEXT

page 1

page 2

page 3

page 4

02/02/2021

Strongly Adaptive OCO with Memory

Recent progress in online control has popularized online learning with m...
06/06/2022

Learning to Control under Time-Varying Environment

This paper investigates the problem of regret minimization in linear tim...
02/16/2022

Online Control of Unknown Time-Varying Dynamical Systems

We study online control of time-varying linear systems with unknown dyna...
02/23/2019

Online Control with Adversarial Disturbances

We study the control of a linear dynamical system with adversarial distu...
02/26/2021

A Regret Minimization Approach to Iterative Learning Control

We consider the setting of iterative learning control, or model-based po...
01/28/2022

A Regret Minimization Approach to Multi-Agent Control

We study the problem of multi-agent control of a dynamical system with k...
07/13/2020

Black-Box Control for Linear Dynamical Systems

We consider the problem of controlling an unknown linear time-invariant ...