Deciding Which Skill to Learn When: Temporal-Difference Competence-Based Intrinsic Motivation (TD-CB-IM)

Intrinsic motivations can be defined by contrasting them to extrinsic motivations. Extrinsic motivations are directed to drive the learning of behavior directed to satisfy basic needs related to the organisms’ survival and reproduction. Intrinsic motivations, instead, are motivations that serve the evolutionary function of acquiring knowledge (e.g., the capacity to predict) and competence (i.e., the capacity to do) in the absence of extrinsic motivations: this knowledge and competence can be later exploited for producing behaviors that enhance biological fitness. Knowledge-based intrinsic motivation mechanisms (KB-IM), usable for guiding learning on the basis of the level or change of knowledge, have been widely modeled and studied. Instead, competence-based intrinsic motivation mechanisms (CB-IM), usable for guiding learning on the basis of the level or improvement of competence, have been much less investigated. The goal of this chapter is twofold. First, it aims to clarify the nature and possible roles of CB-IM mechanisms for learning, in particular in relation to the cumulative acquisition of a repertoire of skills. Second, it aims to review a specific CB-IM mechanism, the Temporal-Difference Competence-Based Intrinsic Motivation (TD-CB-IM). TD-CB-IM measures the improvement rate of skill acquisition on the basis of the Temporal-Difference learning signal (TD error) that is used in several reinforcement learning (RL) models. The effectiveness of the mechanism is supported by reviewing and discussing in depth the results of experiments in which the TD-CB-IM mechanism is successfully exploited by a hierarchical RL model controlling a simulated navigating robot to decide when to train different skills in different environmental conditions. G. Baldassarre ( ) M. Mirolli Laboratory of Computational Embodied Neuroscience, Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche, Rome, Italy e-mail: [email protected]; [email protected] G. Baldassarre and M. Mirolli (eds.), Intrinsically Motivated Learning in Natural and Artificial Systems, DOI 10.1007/978-3-642-32375-1 11, © Springer-Verlag Berlin Heidelberg 2013 257 258 G. Baldassarre and M. Mirolli