Dynamic Games: Theory and Applications

Related lectures (43)

Provides an in-depth analysis of the Random Field Ising Model, covering model description, free entropy, and mean field algorithm.

Covers the proof of the recursive formula for the optimal gains in LQ control over a finite horizon.

Explores challenges in control, safety, and coordination for autonomous systems like autonomous cars, focusing on safe learning and Nash equilibria.

Explores closed-loop control systems, PID regulator design, and performance analysis.

Explores Linear Quadratic optimal control, analyzing cost, and presenting the solution to the FH-LQ problem.

Explores the Model Reference Method for designing PID regulators and cascade controllers for complex systems.

Explores accelerating the Value Iteration algorithm using control theory and matrix splitting techniques to achieve faster convergence.

Discusses closed-loop eigenvalue selection and disturbance impact in multivariable control systems.

Covers Markov Decision Processes, their structure, and their role in reinforcement learning.

Covers zero-sum games, focusing on Nash equilibria, security strategies, and their applications in various scenarios.

Covers potential games, their properties, and the convergence of best-response dynamics to Nash equilibria.

Covers game theory fundamentals, Nash equilibrium, and strategic interactions in multi-agent systems.

Covers Markov games, their dynamics, equilibria, and applications in reinforcement learning.

Covers dynamic games, focusing on backward induction and finding Nash equilibria in two-player scenarios.

Covers the basics of static games and introduces key game theory concepts, including the Prisoner's Dilemma and Nash Equilibrium.

Discusses Stackelberg games and backward induction, illustrating game theory concepts through examples and applications in real-world scenarios.

Explores game theory, decision-making in interdependent environments, equilibrium concepts, and economic applications in management.

Explores multistage games, focusing on extensive form and feedback strategies, including Nash equilibria and backward induction methods.

Covers learning dynamics in finite action games and explores various types of equilibria, including correlated and coarse correlated equilibria.

Provides guidance on project proposals in game theory, showcasing past examples and emphasizing problem formulation and mathematical modeling.

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