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

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