Using Adaptive Intrinsic Motivation in RL to Model Learning Across Development

Abstract

Reinforcement learning is a powerful model of animal learning in brief, controlled experimental conditions, but does not readily explain the development of behavior over an animal's whole lifetime. In this paper, we describe a framework to address this shortcoming by introducing the single-life reinforcement learning setting to cognitive science. We construct an agent with two learning systems: an extrinsic learner that learns within a single lifetime, and an intrinsic learner that learns across lifetimes, equipping the agent with intrinsic motivation. We show that this model outperforms heuristic benchmarks and recapitulates a transition from exploratory to habit-driven behavior, while allowing the agent to learn an interpretable value function. We formulate a precise definition of intrinsic motivation and discuss the philosophical implications of using reinforcement learning as a model of behavior in the real world.