Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
Abstract
Graphs are a natural representation for systems based on relations between connected entities. Combinatorial optimization problems, which arise when considering an objective function related to a process of interest on discrete structures, are often challenging due to the rapid growth of the solution space. The trial-and-error paradigm of Reinforcement Learning has recently emerged as a promising alternative to traditional methods, such as exact algorithms and (meta)heuristics, for discovering better decision-making strategies in a variety of disciplines including chemistry, computer science, and statistics. Despite the fact that they arose in markedly different fields, these techniques share significant commonalities. Therefore, we set out to synthesize this work in a unifying perspective that we term Graph Reinforcement Learning, interpreting it as a constructive decision-making method for graph problems. After covering the relevant technical background, we review works along the dividing line of whether the goal is to optimize graph structure given a process of interest, or to optimize the outcome of the process itself under fixed graph structure. Finally, we discuss the common challenges facing the field and open research questions. In contrast with other surveys, the present work focuses on non-canonical graph problems for which performant algorithms are typically not known and Reinforcement Learning is able to provide efficient and effective solutions.
AI-Generated Overview
Research Overview: Graph Reinforcement Learning for Combinatorial Optimization
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Research Focus: The paper presents a comprehensive survey on Graph Reinforcement Learning (Graph RL), exploring its application to combinatorial optimization problems, especially non-canonical ones where effective traditional algorithms are lacking.
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Methodology: The authors synthesize existing literature on Graph RL by categorizing studies into two main areas: Graph Structure Optimization and Graph Process Optimization, while examining various techniques used for modeling and solving these problems through reinforcement learning frameworks.
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Results: The survey finds that RL approaches, particularly when combined with Graph Neural Networks (GNNs), can outperform conventional heuristic methods in solving non-canonical optimization problems, achieving better performance across various applications.
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Key Contribution(s): The paper consolidates the diverse approaches within Graph RL under a unified framework, highlighting the applicability of machine learning techniques to challenging graph-based combinatorial optimization tasks that have been traditionally neglected.
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Significance: By focusing on non-canonical problems and outlining common challenges and open questions, the work provides valuable insights into the emerging area of Graph RL, potentially paving the way for future advancements in both theoretical and practical applications.
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Broader Applications: The findings are broadly applicable across multiple domains including operations research, molecular design in chemistry, computational social science, and network routing in computer science, demonstrating the versatility of Graph RL techniques in solving real-world optimization problems.
This overview encapsulates the core aspects of the paper, emphasizing its contributions to the field of machine learning and optimization.