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.

通过对智能体控制完成的问题广泛应用增强学习方法进行研究。 基于发展心理学中的内在动机的概念，提出了一个系统性方法来确定内在动机的现有方法。该方法分为三类，分别为互补的内在奖励，探索策略和内在动机的目标。 具有世界模型和内在动机的代理的架构被描述出来，以帮助提高学习能力，同时探讨了在这一领域开发新技术的潜力。