Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review
It provides a unified framework for researchers in reinforcement learning to understand and develop intrinsic motivation techniques, but it is incremental as it reviews and organizes existing methods rather than introducing new ones.
This review tackles the challenge of creating highly autonomous agents in reinforcement learning by exploring intrinsic motivation derived from developmental psychology, proposing a systematic framework that categorizes methods based on how they use a world model in agent components.
The reinforcement learning research area contains a wide range of methods for solving the problems of intelligent agent control. Despite the progress that has been made, the task of creating a highly autonomous agent is still a significant challenge. One potential solution to this problem is intrinsic motivation, a concept derived from developmental psychology. This review considers the existing methods for determining intrinsic motivation based on the world model obtained by the agent. We propose a systematic approach to current research in this field, which consists of three categories of methods, distinguished by the way they utilize a world model in the agent's components: complementary intrinsic reward, exploration policy, and intrinsically motivated goals. The proposed unified framework describes the architecture of agents using a world model and intrinsic motivation to improve learning. The potential for developing new techniques in this area of research is also examined.