Safe Learning for Autonomous Systems

Related lectures (30)

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

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

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

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

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

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

Covers multi-agent systems in traffic management and dynamic pricing strategies using game theory.

Covers intelligent agents, decision-making, planning, machine learning, and game theory.