Intrinsic Motivation

Intrinsic motivation, inspired by how humans and animals learn through exploration and curiosity, aims to improve reinforcement learning agents' ability to learn in complex, reward-sparse environments. Current research focuses on developing effective intrinsic reward functions, often using prediction errors or information-theoretic measures, and incorporating these into various reinforcement learning algorithms, including those employing model-based approaches, graph neural networks, and evolutionary strategies. This work is significant because it addresses a critical limitation of traditional reinforcement learning—the need for carefully designed, dense reward signals—and has implications for improving the sample efficiency and robustness of AI agents across diverse applications, from robotics to educational technology.