Methods of intrinsic motivation in model-based reinforcement learning problems

The reinforcement learning approach offers a wide range of methods for solving problems of intelligent agents’ control. However, the problem of training an agent from sparse rewards remains relevant. One of the possible solutions is to use methods of intrinsic motivation – an idea came from developmental psychology. Intrinsic motivation explains human behavior in the absence of extrinsic control stimulate. In this article, we review the existing methods of determining intrinsic motivation based on the learned world model. The method systematization consisting of three classes is proposed. These classes differ by the application of the word model to agent components: reward system, exploration policy and intrinsic goals. We present a unified framework for describing the architecture of an agent using a world model and intrinsic motivation to improve learning. The prospects for the development in this field of study are analyzed.