Modern Algorithmic Game Theory

NOPT021

Games have long served as benchmarks and marked milestones of progress in artificial intelligence (AI). This course teaches you fundamental formalisms, solution concepts and algorithms. We show how to solve both perfect and imperfect information games, and you will understand the deep connections and application of reinforcement learning to game theory.  At the end of the course, you will implement algorithms to optimally solve (i.e. converge to Nash Equilibria) in small interesting games – notably small poker variants. After going through all the courses, you will get a solid understanding of modern algorithms for solving large scale games (e.g AlphaZero, DeepStack).

Prerequisites

Python (homework assignments primarily involve implementing algorithms); a basic understanding of optimization (convex functions, local/global optima)

Not required, but can be helpful: Linear optimization/programming; familiarity with reinforcement learning methods (MDPs, rewards)

Details

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