# Solve Traveling Salesman Problem by Monte Carlo Tree Search and Deep Neural Network

arXiv: https://arxiv.org/abs/2005.06879 · HTML: https://mlsanity.com/paper/2005.06879/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 7 of 7)

- [Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications](https://mlsanity.com/paper/2303.08060/) (2023)
- [Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization](https://mlsanity.com/paper/2305.05812/) (2023)
- [Maximum Independent Set: Self-Training through Dynamic Programming](https://mlsanity.com/paper/2310.18672/) (2023)
- [A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems](https://mlsanity.com/paper/2305.01883/) (2023)
- [Policy-Based Self-Competition for Planning Problems](https://mlsanity.com/paper/2306.04403/) (2023)
- [Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem](https://mlsanity.com/paper/2107.06870/) (2021)
- [Learn to Tour: Operator Design For Solution Feasibility Mapping in Pickup-and-delivery Traveling Salesman Problem](https://mlsanity.com/paper/2404.11458/) (2024)

## Abstract (arXiv)

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.