  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 a variety of TSP
instances with different types and sizes. Results suggest that our Solvers
achieve the state-of-the-art performance even on tasks the Solver never meets,
whilst the performance of other deep learning-based Solvers drops sharply due
to over-fitting. To demonstrate the principle of our framework, we study the
learning outcome of the proposed two-player game and demonstrate that the
exploitability of the Solver population decreases during training, and it
eventually approximates the Nash equilibrium along with the Generator.
