Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2201.04895v1
• Date: Thu, 13 Jan 2022 11:36:05 GMT
• Title: Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning
• Authors: Udesh Gunarathna, Renata Borovica-Gajic, Shanika Karunasekara, Egemen Tanin
• Abstract summary: In recent years, using deep learning techniques to find solutions for NP-hard graph problems has gained much interest. In this paper, we propose a novel architecture named Graph Temporal Attention with Reinforcement Learning (GTA-RL) to learn solutions for graph-based dynamic optimization problems.
• Score: 1.3534683694551497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph problems such as traveling salesman problem, or finding minimal Steiner trees are widely studied and used in data engineering and computer science. Typically, in real-world applications, the features of the graph tend to change over time, thus, finding a solution to the problem becomes challenging. The dynamic version of many graph problems are the key for a plethora of real-world problems in transportation, telecommunication, and social networks. In recent years, using deep learning techniques to find heuristic solutions for NP-hard graph combinatorial problems has gained much interest as these learned heuristics can find near-optimal solutions efficiently. However, most of the existing methods for learning heuristics focus on static graph problems. The dynamic nature makes NP-hard graph problems much more challenging to learn, and the existing methods fail to find reasonable solutions. In this paper, we propose a novel architecture named Graph Temporal Attention with Reinforcement Learning (GTA-RL) to learn heuristic solutions for graph-based dynamic combinatorial optimization problems. The GTA-RL architecture consists of an encoder capable of embedding temporal features of a combinatorial problem instance and a decoder capable of dynamically focusing on the embedded features to find a solution to a given combinatorial problem instance. We then extend our architecture to learn heuristics for the real-time version of combinatorial optimization problems where all input features of a problem are not known a prior, but rather learned in real-time. Our experimental results against several state-of-the-art learning-based algorithms and optimal solvers demonstrate that our approach outperforms the state-of-the-art learning-based approaches in terms of effectiveness and optimal solvers in terms of efficiency on dynamic and real-time graph combinatorial optimization.

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