Adaptive organization of generalized behavioral concepts for autonomous robots: schema-based modular reinforcement learning

In this paper, we introduce a reinforcement learning method for autonomous robots to obtain generalized behavioral concepts. Reinforcement learning is a well formulated method for autonomous robots to obtain a new behavioral concept by themselves. However, these behavioral concepts cannot be applied to other environments that are different from the place where the robots have learned the concepts. On the contrary, we, human beings, can apply our behavioral concepts to some different environments, objects, and/or situations. We think this ability owes to some memory structure like schema system that was originally proposed by J. Piaget. We previously proposed a modular-learning method called Dual-Schemata model. In this paper, we add a reinforcement learning mechanism to this model. By being provided with this structure, autonomous robots become able to obtain new generalized behavioral concepts by themselves. We also show this kind of structure enables autonomous robots to behave appropriately even in a novel socially interactive environment.