Enhancing Reinforcement Learning through Graph Neural Networks: A Novel Approach

Abstract

Reinforcement Learning (RL) has showcased remarkable success in various domains. However, its performance often degrades in the environment with complex structures and distributed rewards. Graph-Based Reinforcement Learning (GBRL) is an approach that combines the strengths of Graph Theory with Reinforcement Learning to optimize complex decision making problems in any networked system. This paper proposes an approach of integrating Reinforcement Learning approaches with Graph Neural Networks (GNNs)to enhance the learning pipeline and model structured data by utilising their capacity. We present an approach that uses GNNs represented as graphs that enables RL agents to get dependencies between entities and access information through them. This paper exhibits GBRL techniques and their application in different domains. A framework of GBRL methods and its advantages over RL methods in working on graph-based data. This work highlights the synergy between graph-based learning and decision-making, offering a promising direction for solving high-dimensional and structured RL tasks more effectively. We also summarize the key challenges and the open research directions in this field.