Systematic Review of GAN for Enhancing Efficiency in AI in Gaming

A thorough analysis of Generative Adversarial Networks (GANs) and how they might be used to improve AI gaming efficiency is presented in this research. Game developers now have never-before-seen possibilities for innovation, immersion, and personalization thanks to GANs, which have become a potent tool for producing realistic data samples across a variety of fields. This review investigates the architecture, training methods, applications, difficulties, and potential future directions of GANs in AI gaming through a thorough analysis of the body of research and publications. The review demonstrates how GANs can transform the game industry by highlighting their many uses in procedural content generation, texture synthesis, character animation, environment design, and content customization. Nevertheless, there are certain difficulties in integrating GANs into AI games, such as computational resources, mode collapse, training instability, and assessment metrics.as well as legal matters. To tackle these obstacles, multidisciplinary research endeavors and cooperation across academic institutions, corporate entities, and regulatory agencies are necessary. Going forward, new avenues for study and developments in GAN technology hold great potential for getting over these roadblocks and opening up fresh possibilities in AI gaming. We can create a future where AI powered gaming experiences change the parameters of interactive entertainment and provide players globally with more engaging, varied, and personalized gaming experiences by utilizing the possibilities of GANs and addressing the related obstacles.