Learning arm´s posture control using reinforcement learning and feedback-error-learning

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.