Learning Personalized Optimal Control for Repeatedly Operated Systems

TL;DR

This paper introduces an online learning method for personalized optimal control in systems operated repeatedly under parametric uncertainty, adapting control strategies based on stochastic system parameters.

Contribution

It proposes a novel approach to personalize control inputs by learning from repeated interactions with uncertain system dynamics, improving control performance over time.

Findings

Effective control personalization demonstrated in simulation

Improved control performance under parametric uncertainty

Adaptive learning approach outperforms non-personalized methods

Abstract

We consider the problem of online learning of optimal control for repeatedly operated systems in the presence of parametric uncertainty. During each round of operation, environment selects system parameters according to a fixed but unknown probability distribution. These parameters govern the dynamics of a plant. An agent chooses a control input to the plant and is then revealed the cost of the choice. In this setting, we design an agent that personalizes the control input to this plant taking into account the stochasticity involved. We demonstrate the effectiveness of our approach on a simulated system.