Game Theory and Deep Learning: Fundamentals and Applications is the first book to establish a clear and comprehensive link between game theory and deep learning, demonstrating the critical importance of this interplay for solving key technological challenges such as those found in future wireless and energy networks. It delves into the latest connections between game theory and generative AI, including large language models, showcasing how these advanced concepts can be harnessed to address complex real-world problems. Readers will gain a deep understanding of how these two powerful fields intersect and the practical applications of this knowledge.

The book teaches readers how to develop a solid foundation in game theory and understand interactive scenarios in both engineering and everyday life, effectively apply their knowledge to practical problems, including resource allocation, security, and influence maximization, and finally, design strategies that optimally exploit available information through successive optimization, reinforcement learning, deep learning, and generative AI techniques.

• Gives applications of game theory and deep learning across various domains, such as future wireless and energy networks
• Explores the burgeoning connections between game theory and generative AI, including large language models, providing readers with insights into the most current technological advancements
• Gives practical methodologies for studying games and to develop design strategies that are supported by numerous examples and detailed case studies
• Provides an in-depth overview of outstanding research challenges and potential future directions in the interplay between game theory and deep learning, helping readers remain current and identify new areas for exploration and innovation
• Includes guidance on implementation with code snippets and a detailed presentation of methods