Knowledge graph (KG) is a form of data representation that uses graph structure to model the connections between things. It is an important foundation for realizing cognitive intelligence and has received extensive attention from academia and industry. The research of knowledge graph includes four parts: knowledge representation, knowledge extraction, knowledge fusion, knowledge reasoning. Currently, there are still some challenges in the research of knowledge graphs. For example, knowledge extraction methods face difficulty in obtaining labeled data, while distantly supervised training samples have noise problems. The interpretability and reliability of the knowledge reasoning methods need to be further improved. Knowledge representation methods also have problems such as relying on manually defined rules or prior knowledge. Knowledge fusion methods fail to fully model the interdependence between entities. Environment-driven reinforcement learning (RL) algorithms are suitable for sequential decision-making problems. By modeling the research problem of the knowledge graph into a path (sequence) problem, and applying reinforcement learning methods, the above-mentioned problems in the knowledge graph can be solved, which has important application value. The basic knowledge of KG and RL are introduced firstly. Secondly, a research of KG based on RL are comprehensively reviewed. Then, it focuses on how the KG method based on RL can be applied to practical application areas such as intelligent recommendation, conversation system, game, biology, medicine prediction, finance and cybersecurity. Finally, the future directions of KG and RL are discussed in detail.