  Deep reinforcement learning (DRL) has empowered a variety of artificial
intelligence fields, including pattern recognition, robotics,
recommendation-systems, and gaming. Similarly, graph neural networks (GNN) have
also demonstrated their superior performance in supervised learning for
graph-structured data. In recent times, the fusion of GNN with DRL for
graph-structured environments has attracted a lot of attention. This paper
provides a comprehensive review of these hybrid works. These works can be
classified into two categories: (1) algorithmic enhancement, where DRL and GNN
complement each other for better utility; (2) application-specific enhancement,
where DRL and GNN support each other. This fusion effectively addresses various
complex problems in engineering and life sciences. Based on the review, we
further analyze the applicability and benefits of fusing these two domains,
especially in terms of increasing generalizability and reducing computational
complexity. Finally, the key challenges in integrating DRL and GNN, and
potential future research directions are highlighted, which will be of interest
to the broader machine learning community.
