A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
arXiv:2002.03282
Abstract
Recent researches show that machine learning has the potential to learn better heuristics than the one designed by human for solving combinatorial optimization problems. The deep neural network is used to characterize the input instance for constructing a feasible solution incrementally. Recently, an attention model is proposed to solve routing problems. In this model, the state of an instance is represented by node features that are fixed over time. However, the fact is, the state of an instance is changed according to the decision that the model made at different construction steps, and the node features should be updated correspondingly. Therefore, this paper presents a dynamic attention model with dynamic encoder-decoder architecture, which enables the model to explore node features dynamically and exploit hidden structure information effectively at different construction steps. This paper focuses on a challenging NP-hard problem, vehicle routing problem. The experiments indicate that our model outperforms the previous methods and also shows a good generalization performance.
15 pages, 8 figures
References in corpus (6)
- Sequence to Sequence Learning with Neural Networks
- Neural Message Passing for Quantum Chemistry
- Learning Combinatorial Optimization Algorithms over Graphs
- Discriminative Embeddings of Latent Variable Models for Structured Data
- Attention, Learn to Solve Routing Problems!
- Learning Heuristics over Large Graphs via Deep Reinforcement Learning