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

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Book cover Intrinsically Motivated Learning in Natural and Artificial Systems

Abstract

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.

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Notes

  1. 1.

    Reinforcement learning models mimic the trial-and-error learning processes of animals directed to achieve an extrinsic reward, in particular those studied by behaviorist psychology with instrumental learning paradigms (Lieberman 1993) (but the models are also used to capture some mechanisms of Pavlovian learning). One of the most biologically plausible RL models, the actor-critic RL model (Houk et al. 1995; Sutton and Barto 1998), is formed by (a) an actor, which progressively learns to select actions so to maximize rewards, and (b) a critic, which progressively learns to assign an evaluation (an estimate of future rewards) to each state on the basis of the received rewards (the actor is trained to act so as to move the agent toward states with higher evaluations).

  2. 2.

    By “hierarchical” we mean that some components of the system, usually processing information at a more abstract level, exert an influence on other components, usually processing information at more detailed level. By “modular” we mean that different chunks of behavior are encoded in different portions of the system. Modularity can be either “structural,” that is, related to strong connections within groups of neurons and looser connections between groups, or “functional,” for example leading to encode different chunks of behavior within different portions of a rather homogeneous system on the basis of specific self-organizing mechanisms.

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Acknowledgements

This research has received funds from the European Commission 7th Framework Programme (FP7/2007-2013), “Challenge 2 - Cognitive Systems, Interaction, Robotics,” Grant Agreement No. ICT-IP-231722, Project “IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots.”

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  1. Laboratory of Computational Embodied Neuroscience, Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche, Rome, Italy

    Gianluca Baldassarre & Marco Mirolli

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  1. Gianluca Baldassarre
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Correspondence to Gianluca Baldassarre .

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Baldassarre, G., Mirolli, M. (2013). Deciding Which Skill to Learn When: Temporal-Difference Competence-Based Intrinsic Motivation (TD-CB-IM). In: Baldassarre, G., Mirolli, M. (eds) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32375-1_11

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