Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Game-Theoretic Approach for Improving Generalization Ability of TSP
  Solvers   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.

17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol