Boosting Exploration in Reinforcement Learning Agents via Path-Based Knowledge Graph Reasoning

This paper proposes and presents a novel methodology for enhancing the exploration process in reinforcement learning algorithms, based on applying an optimal path search algorithm on a knowledge graph. Traditional approaches in the exploration phase used in agent-based models often rely on random and probabilistic strategies, which may prove inefficient in complex and dynamic environments. This work introduces an alternative approach that leverages structured information from a knowledge graph to identify and select the most promising actions. The methodology includes a path-based reasoning module that uses the knowledge graph to determine suitable action directions for the agent. Experimental results indicate that the proposed method improves agent performance in complex, dynamic environments with non-deterministic action sets, demonstrating superior results in tasks with complex knowledge structures and high adaptation requirements.