Lectures related to Markov Games: Concepts and Applications in Reinforcement Learning

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

Potential Games: Best-Response Dynamics and Equilibria

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

Static Games: Introduction to Game Theory Concepts

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

Game Theory: Stackelberg and Backward Induction

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

Zero-Sum Games: Concepts and Applications

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

Multistage Games: Extensive Form and Feedback Strategies

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

Safe Learning for Autonomous Systems

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

Potential Games: Best Response Dynamics and Nash Equilibria

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

Markov Decision Processes: Foundations of Reinforcement Learning

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

Game Theory: Learning in Finite Action Games

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

Game Theory Applications: Project Proposals and Examples

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

Real World Games: Negotiation Strategies

Delves into real-world games, negotiation strategies, and optimal agreements beyond Nash equilibrium.

Introduction to Reinforcement Learning: Key Concepts and Applications

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.

Multi-Agent Systems: Traffic Management and Pricing Strategies

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

Dynamic Games: Theory and Applications

Covers dynamic games, focusing on their structure, applications, and the transition from tree to loop models.

Infinite-Horizon Problems: Formulation & Complexity

Covers infinite-horizon problems in Applied Probability and Stochastic Processes.

Convex Games: Applications and Equilibria

Covers convex games, their equilibria, and applications in various fields such as traffic and electricity markets.

Dynamic Games: Backward Induction and Nash Equilibria

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

Game Theory: Decision Making in Interdependent Environments

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

Descent methods and line search: Finiteness of the line search algorithm

Explores the Wolfe conditions for line search algorithms and proves the finiteness of the line search parameter.