title: Learning arm's posture control using reinforcement learning and feedback-error-learning.
abstract: In this paper, we propose a learning model using the Actor-Critic method and the feedback-error-learning scheme. The Actor-Critic method, which is one of the major frameworks in reinforcement learning, has attracted attention as a computational learning model in the basal ganglia. Meanwhile, the feedback-error-learning is learning architecture proposed as a computationally coherent model of cerebellar motor learning. This learning architecture's purpose is to acquire a feed-forward controller by using a feedback controller's output as an error signal. In past researches, a predetermined constant gain feedback controller was used for the feedback-error-learning. We use the Actor-Critic method for obtaining a feedback controller in the feedback-error-earning. By applying the proposed learning model to an arm's posture control, we show that high-performance feedback and feed-forward controller can be acquired from only by using a scalar value of reward.
authors.affiliation: Dept. of Comput. Intelligence & Syst. Sci., Interdisciplinary Graduate School of Science and Engineering, Tokyo Inst. of Technol., Japan.
authors.forename: H
authors.initials: H
authors.lastname: Kambara
authors.forename: J
authors.initials: J
authors.lastname: Kim
authors.forename: M
authors.initials: M
authors.lastname: Sato
authors.forename: Y
authors.initials: Y
authors.lastname: Koike