We propose a manager-worker framework (the implementation of our model is publically
available at: https://github. com/zcaicaros/manager-worker-mtsptwr) based on deep …

In this work, we introduce Graph Pointer Networks (GPNs) trained using reinforcement
learning (RL) for tackling the traveling salesman problem (TSP). GPNs build upon Pointer …

Abstract Traveling Salesman Problem (TSP), as a classic routing optimization problem
originally arising in the domain of transportation and logistics, has become a critical task in …

Traveling salesman problem (TSP) is a classical combinatorial optimization problem. As it
represents a large number of important practical problems, it has received extensive studies …

For the traveling salesman problem (TSP), it is usually hard to find a high-quality solution in
polynomial time. In the last two years, graph neural networks emerge as a promising …

We propose an end-to-end learning framework based on hierarchical reinforcement
learning, called H-TSP, for addressing the large-scale Traveling Salesman Problem (TSP) …

We propose ScheduleNet, a scalable scheduler that minimizes the task completion time by
coordinating multiple agents. We formulate the min-max multiple traveling salesmen …

The multiple traveling salesman problem (mTSP) is a well-known NP-hard problem with
numerous real-world applications. In particular, this work addresses MinMax mTSP, where …

Solutions to the Traveling Salesperson Problem (TSP) have practical applications to
processes in transportation, logistics, and automation, yet must be computed with minimal …

Recent research has showen that deep reinforcement learning (DRL) can be used to design
better heuristics for the traveling salesman problem (TSP) on the small scale, but does not …