2022/05/20 by Jorge Ramírez‐Ruiz, Ramírez-Ruiz, Jorge, Dmytro Grytskyy +4 · 7 citations
Biochemistry, Genetics and Molecular Biology · Neuroscience · Social Sciences · #Artificial Intelligence (cs.AI) #Evolutionary Game Theory and Cooperation #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Zebrafish Biomedical Research Applications
paper · pdf · doi:10.48550/arxiv.2205.10316
openalex publication_date 2022/05/20 · openalex created_date 2022/05/25 · openalex updated_date 2026/07/28
Most theories of behavior posit that agents tend to maximize some form of reward or utility. However, animals very often move with curiosity and seem to be motivated in a reward-free manner. Here we abandon the idea of reward maximization, and propose that the goal of behavior is maximizing occupancy of future paths of actions and states. According to this maximum occupancy principle, rewards are the means to occupy path space, not the goal per se; goal-directedness simply emerges as rational ways of searching for resources so that movement, understood amply, never ends. We find that action-state path entropy is the only measure consistent with additivity and other intuitive properties of expected future action-state path occupancy. We provide analytical expressions that relate the optimal policy and state-value function, and prove convergence of our value iteration algorithm. Using discrete and continuous state tasks, including a high--dimensional controller, we show that complex behaviors such as `dancing', hide-and-seek and a basic form of altruistic behavior naturally result from the intrinsic motivation to occupy path space. All in all, we present a theory of behavior that generates both variability and goal-directedness in the absence of reward maximization.