  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.
