论文标题
使用动态注意模型用于车辆路由问题的深入增强学习算法
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
论文作者
论文摘要
最近的研究表明，机器学习具有比人类为解决组合优化问题设计的启发式方法学习更好的潜力。深神经网络用于表征用于逐步构建可行解决方案的输入实例。最近，提出了一个注意模型来解决路由问题。在此模型中，实例的状态由固定的节点特征表示。但是，事实是，一个实例的状态根据在不同构造步骤中制定的决定以及节点功能应相应更新的决定更改。因此，本文使用动态编码器架构进行了动态注意模型，该模型使该模型能够动态探索节点特征，并在不同的构造步骤下有效地利用隐藏的结构信息。本文重点介绍了一个具有挑战性的NP问题，车辆路由问题。实验表明，我们的模型表现优于先前的方法，并且还显示出良好的概括性能。
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.
