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Title: Intrinsic Motivation in Dynamical Control Systems
Biological systems often choose actions without an explicit reward signal, a phenomenon known as intrinsic motivation. The computational principles underlying this behavior remain poorly understood. In this study, we investigate an information-theoretic approach to intrinsic motivation, based on maximizing an agent's empowerment (the mutual information between its past actions and future states). We show that this approach generalizes previous attempts to formalize intrinsic motivation, and we provide a computationally efficient algorithm for computing the necessary quantities. We test our approach on several benchmark control problems, and we explain its success in guiding intrinsically motivated behaviors by relating our information-theoretic control function to fundamental properties of the dynamical system representing the combined agent-environment system. This opens the door for designing practical artificial, intrinsically motivated controllers and for linking animal behaviors to their dynamical properties. Published by the American Physical Society2024  more » « less
Award ID(s):
2513350
PAR ID:
10582457
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
PRX Life, American Physics Society.
Date Published:
Journal Name:
PRX Life
Volume:
2
Issue:
3
ISSN:
2835-8279
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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