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Deep reinforcement learning (DRL) has empowered a variety of artificialintelligence fields, including pattern recognition, robotics,recommendation-systems, and gaming. Similarly, graph neural networks (GNN) havealso demonstrated their superior performance in supervised learning forgraph-structured data. In recent times, the fusion of GNN with DRL forgraph-structured environments has attracted a lot of attention. This paperprovides a comprehensive review of these hybrid works. These works can beclassified into two categories: (1) algorithmic enhancement, where DRL and GNNcomplement each other for better utility; (2) application-specific enhancement,where DRL and GNN support each other. This fusion effectively addresses variouscomplex problems in engineering and life sciences. Based on the review, wefurther analyze the applicability and benefits of fusing these two domains,especially in terms of increasing generalizability and reducing computationalcomplexity. Finally, the key challenges in integrating DRL and GNN, andpotential future research directions are highlighted, which will be of interestto the broader machine learning community.

Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications