一种分层强化学习的知识推理方法.

In the process of knowledge inference, with the increase of the length of the inference path, the action space of the node increases sharply, which makes the inference difficulty continue to increase. A Knowledge Reasoning Method Of Hierarchical Reinforcement Learning (MutiAg-HRL) is proposed to reduce the size of action space in the reasoning process. MutiAg-HRL invokes high-level agents to perform rough reasoning on the relationships in the knowledge graph, and determines the approximate location of the target entity by calculating the similarity between the next step relationship and the given query relationship. According to the relationship given by the high-level agent, the low-level agents are guided to conduct detailed reasoning and select the next action. The model also constructs an interactive reward mechanism to reward the relationship between the two agents and the choice of actions in time to prevent the problem of sparse reward in the model. To verify the effectiveness of the proposed method, experiments were carried out on FB15K-237 and NELL-995 datasets. The experimental results were compared with those of 11mainstream methods such as TransE, MINERVA and HRL. The average value of the MutiAg-HRL method on the link prediction task Hits@k was increased by 1.85%. MRR increases by an average of 2%. [ABSTRACT FROM AUTHOR]