CCGnet: A deep learning approach to predict Nash equilibrium of chance-constrained games

Abstract

This paper proposes a novel method for efficiently finding the Nash equilibrium in a chance-constrained game (CCG). Conventional numerical solution methods require significant computational time when solving multiple instances of CCG. We introduce CCGnet, a deep learning approach that is capable of efficiently solving multiple instances of CCG in a one-shot manner. CCGnet employs a specialized network structure and training algorithm based on neurodynamic optimization. We demonstrate the strong performance of CCGnet in practice and show that our proposed method outperforms conventional methods.