A Bayesian Reinforcement Learning Method for Periodic Robotic Control Under Significant Uncertainty

Abstract

This paper addresses the lack of research on periodic reinforcement learning for physical robot control by presenting a 3-phase periodic Bayesian reinforcement learning method for uncertain environments. Drawing on cognition theory, the proposed approach achieves effective convergence with fewer training episodes. The coach-based demonstration phase narrows the search space and establishes a foundation for a coarse-to-fine control strategy. The reconnaissance phase enhances adaptability by discovering a valuable global repre-sentation, and the operation phase produces accurate robotic control by applying the learned representation and periodically updating local information. Comparative analysis with state-of-the-art methods validates the efficacy of our approach on exemplar control tasks in simulation and a biomedical project involving a simulated cranial window task.