Game theory has significant applications in technology, especially concerning computer science, electronics, aerospace engineering, etc. It has several applications in various domains. Game theory is an essential field for research, and it helps to choose the suitable strategy of the players in a game. The current study also details various real-time applications of existing literature, valuable datasets in the field, and the popularity of this research area in recent years of publications and citations. This paper will give researchers an extensive account of significant contributions which have taken place in deep learning using game-theoretic concepts thus, giving a clear insight, challenges, and future directions. The training of the generators and discriminators in GANs is essentially a two-player zero-sum game that allows the model to learn complex functions. Generative Adversarial Network (GAN) is a deep learning architecture that has gained popularity in solving complex computer vision problems. Most of the classification problems which popularly employ a deep learning approach can be seen as a Stackelberg game.

The design of deep learning models often involves a game-theoretic approach. Existing research contributions demonstrate that game theory is a potential approach to improve results in deep learning models. Game theory helps to model or solve various deep learning-based problems. The application of game theory to deep learning includes another dimension in research. Alternatively, game theory has been showing its multi-dimensional applications in the last few decades. Today, deep learning is a fast-evolving area for research in the domain of artificial intelligence.

This paper provides a comprehensive overview of the applications of game theory in deep learning.

Real life examples of game theory