论文标题
解决旅行推销员问题，蒙特卡洛树搜索和深度神经网络
Solve Traveling Salesman Problem by Monte Carlo Tree Search and Deep Neural Network
论文作者
论文摘要
我们提出了一种自学方法，该方法结合了深度强化学习和蒙特卡洛树搜索，以解决旅行推销员的问题。拟议的方法具有两个优势。首先，它采用深度强化学习来计算决策的价值功能，从而消除了手工制作的功能和标记数据的需求。其次，它使用Monte Carlo树搜索来通过比较不同的价值功能来选择最佳策略，从而提高其泛化能力。实验结果表明，该提出的方法在小型至中等问题设置中对其他方法的表现有利。在大问题设置中，它显示出可比的性能与最先进的表现。
We present a self-learning approach that combines deep reinforcement learning and Monte Carlo tree search to solve the traveling salesman problem. The proposed approach has two advantages. First, it adopts deep reinforcement learning to compute the value functions for decision, which removes the need of hand-crafted features and labelled data. Second, it uses Monte Carlo tree search to select the best policy by comparing different value functions, which increases its generalization ability. Experimental results show that the proposed method performs favorably against other methods in small-to-medium problem settings. And it shows comparable performance as state-of-the-art in large problem setting.
