Learning telic-controllable state representations

원문 초록

Computational descriptions of purposeful behavior comprise both descriptive and normative} aspects. The former are used to ascertain current (or future) states of the world and the latter to evaluate the desirability, or lack thereof, of these states under some goal. In Reinforcement Learning, the normative aspect (reward and value functions) is assumed to depend on a predefined and fixed descriptive one (state representation). Alternatively, these two aspects may emerge interdependently: goals can be, and indeed often are, approximated by state-dependent reward functions, but they may also shape the acquired state representations themselves. Here, we present a novel computational framework for state representation learning in bounded agents, where descriptive and normative aspects are coupled through the notion of goal-directed, or telic, states. We introduce the concept of telic controllability to characterize the tradeoff between the granularity of a telic state representation and the policy complexity required to reach all telic states. We propose an algorithm for learning controllable state representations, illustrating it using a simple navigation task with shifting goals. Our framework highlights the crucial role of deliberate ignorance -- knowing which features of experience to ignore -- for learning state representations that balance goal flexibility and policy complexity. More broadly, our work advances a unified theoretical perspective on goal-directed state representation learning in natural and artificial agents.