Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization. (arXiv:2205.13209v1 [cs.LG])

Deep reinforcement learning (DRL)-based combinatorial optimization (CO)
methods (i.e., DRL-NCO) have shown significant merit over the conventional CO
solvers as DRL-NCO is capable of learning CO solvers without supervised labels
attained from the verified solver. This paper presents a novel training scheme,
Sym-NCO, that achieves significant performance increments to existing DRL-NCO
methods. Sym-NCO is a regularizer-based training scheme that leverages
universal symmetricities in various CO problems and solutions. Imposing
symmetricities such as rotational and reflectional invariance can greatly
improve generalization …

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