A novel dynamic priority-based action-selection-mechanism integrating a reinforcement learning

Abstract

A novel action-selection-mechanism is proposed to deal with sequential behaviors, where associations between some of stimulus and behaviors would be learned by a shortest-path-finding-based reinforcement learning technique. To be specific, we define behavioral motivation as a primitive node for action selection, and then sequentially construct a network with behavioral motivations. The vertical path of the network represents a behavioral sequence. Here, such a tree for our proposed ASM can be newly generated and/or updated, whenever a new sequential behaviors is learned. To show the validity of our proposed ASM, some experimental results on a "pushing-box-into-a-goal (PBIG) task" of a mobile robot is illustrated.