  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.
