Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem

2020-12-08Code Available1· sign in to hype

Jiongzhi Zheng, Kun He, Jianrong Zhou, Yan Jin, Chu-min Li

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github.com/JHL-HUST/VSR-LKH
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Abstract

We address the Traveling Salesman Problem (TSP), a famous NP-hard combinatorial optimization problem. And we propose a variable strategy reinforced approach, denoted as VSR-LKH, which combines three reinforcement learning methods (Q-learning, Sarsa and Monte Carlo) with the well-known TSP algorithm, called Lin-Kernighan-Helsgaun (LKH). VSR-LKH replaces the inflexible traversal operation in LKH, and lets the program learn to make choice at each search step by reinforcement learning. Experimental results on 111 TSP benchmarks from the TSPLIB with up to 85,900 cities demonstrate the excellent performance of the proposed method.

Tasks

Combinatorial Optimization Q-Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Traveling Salesman Problem

Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem

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