Foundations of Multiagent Learning and Game Theory

In single-agent environments, training an AI is relatively straightforward, but when multiple learning agents interact, their competing objectives create complex and unpredictable dynamics. Understanding how these agents learn, adapt, and reach stable decisions is key to building the next generation of collaborative and competitive AI systems. This course guides you through the foundational mathematical frameworks, game-theoretic concepts, and optimization strategies needed to design and analyze multiagent systems. You will transition from reading basic theoretical definitions to understanding the underlying mechanics of modern multiagent AI breakthroughs. What you'll learn: Understand fundamental game theory principles, including matrix games, Nash equilibria, and utility functions; Analyze imperfect information games and structured environments like stochastic and polymatrix games; Learn how optimization algorithms and gradient-based methods function when multiple agents learn simultaneously; Explore computational complexity challenges and the mathematical limits of finding equilibria in multiagent systems; Examine modern multiagent reinforcement learning approaches and decentralized coordination frameworks; Apply theoretical concepts to real-world scenarios, understanding how these models power superhuman AI in strategy games. The course begins with core terminology and foundational matrix games before progressing to complex, multi-agent dynamics and modern optimization techniques. Through clear, written explanations and conceptual exercises, you will build a solid theoretical foundation in multiagent systems. This introductory text-based course is designed for aspiring AI researchers, software engineers, and data scientists who want to understand multiagent systems from scratch, with no advanced prerequisites required. Start reading today to unlock the principles behind collective machine intelligence.