Web: http://arxiv.org/abs/2110.15105

May 5, 2022, 1:12 a.m. | Chenguang Wang, Yaodong Yang, Oliver Slumbers, Congying Han, Tiande Guo, Haifeng Zhang, Jun Wang

cs.LG updates on arXiv.org arxiv.org

In this paper, we introduce a two-player zero-sum framework between a
trainable \emph{Solver} and a \emph{Data Generator} to improve the
generalization ability of deep learning-based solvers for Traveling Salesman
Problem (TSP). Grounded in \textsl{Policy Space Response Oracle} (PSRO)
methods, our two-player framework outputs a population of best-responding
Solvers, over which we can mix and output a combined model that achieves the
least exploitability against the Generator, and thereby the most generalizable
performance on different TSP tasks. We conduct experiments on …

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