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Found 2 papers, 1 papers with code
Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time
Combinatorial optimization algorithms for graph problems are usually designed afresh for each new problem with careful attention by an expert to the problem structure.
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning
Furthermore, to approximate solutions to constrained combinatorial optimization problems such as the TSP with time windows, we train hierarchical GPNs (HGPNs) using RL, which learns a hierarchical policy to find an optimal city permutation under constraints.
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Traveling Salesman Problem
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