Personalized control system via reinforcement learning: maximizing utility based on user ratings

Abstract

In this paper, we address the design of personalized control systems, which pursue individual objectives defined for each user. To this end, a problem of reinforcement learning is formulated where an individual objective function is estimated based on the user rating on his/her current control system and its corresponding optimal controller is updated. The novelty of the problem setting is in the modelling of the user rating. The rating is modelled by a quantization of the user utility gained from his/her control system, defined by the value of the objective function at his/her control experience. We propose an algorithm of the estimation to update the control law. Through a numerical experiment, we find out that the proposed algorithm realizes the personalized control system.

Keywords

• personalization
• user rating
• optimal control
• reinforcement learning
• maximizing utility