Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
This addresses the problem of efficiently solving TSP for optimization and logistics, but it is incremental as it builds on existing deep learning approaches by focusing on improvement heuristics.
The paper tackled the Traveling Salesman Problem by learning a 2-opt local search heuristic using deep reinforcement learning, resulting in policies that improve solutions faster and approach near-optimal performance compared to previous deep learning methods.
Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance. However, few studies have focused on improvement heuristics, where a given solution is improved until reaching a near-optimal one. In this work, we propose to learn a local search heuristic based on 2-opt operators via deep reinforcement learning. We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution. Moreover, we introduce a policy neural network that leverages a pointing attention mechanism, which unlike previous works, can be easily extended to more general k-opt moves. Our results show that the learned policies can improve even over random initial solutions and approach near-optimal solutions at a faster rate than previous state-of-the-art deep learning methods.