Addressing GNN Limitations via Deep Reinforcement Learning: Principles, Patterns, and Design Insights

Graph Neural Networks (GNNs) face fundamental limitations that resist gradient-based optimization: static processing paradigms, fixed aggregation schemes, and intractable optimization spaces involving discrete decisions and non-differentiable objectives. These constraints create critical bottlenecks in scalability, generalization, explainability, and heterogeneous data handling. This survey establishes Deep Reinforcement Learning (DRL) as a principled solution by formulating GNN challenges as Markov Decision Processes (MDPs). Learned policies enable adaptive control-dynamically selecting neighbors to aggregate, determining when to stop message passing, and constructing optimal graph topologies. We identify a unified framework revealing the GNN's dual role as both optimization target and state encoder, enabling system-level optimization beyond gradient descent. Analyzing 60+ papers, we provide a systematic blueprint for practitioners, distilling reusable patterns in MDP design and a decision flowchart that maps specific GNN limitations to optimal RL strategies. Empirical validation demonstrates substantial gains across these limitations; for instance, resolving scalability and efficiency bottlenecks yields 87.9% queue reduction in network control and 54.9% faster completion in distributed systems, while overcoming structural generalization limits drives up to 10% accuracy improvements. Finally, we identify crucial research gaps regarding sample efficiency and causal fidelity, while highlighting emerging solutions like Graph World Models and GFlowNets to bridge these divides