Human-behavior learning: a new complementary learning perspective for optimal decision making controllers

Abstract

This paper reviews an almost new method for the design of optimal decision making controllers named as Human-Behavior learning. This new paradigm is inspired by the complementary learning that different areas of the human brain have to improve learning and experience transference. It is shown that independent and well identified sources of knowledge can enhance learning and facilitate the design of the optimal decision making controller. This interaction is modelled as a Markov Decision Process defined by a tuple of actions, cognitions, and emotions sets. Existing methods of both control and reinforcement learning theories are reviewed and connected to complete the behavior learning picture for a class of linear systems.