Markov Games: Concepts and Applications in Reinforcement Learning

Related lectures (32)

Page 1 of 4

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

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

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.

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

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

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

Covers potential games, best response dynamics, and the convergence to Nash equilibria.

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

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