Combinatorial game theory

Related lectures (32)

Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.

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

Explores zero-sum games and the minimax theorem in Game Theory, emphasizing optimal strategies.

Delves into building robust ensembles through margin boosting for improved adversarial defense in machine learning models.

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

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

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

Explores Farkas' Lemma, hyperplane separation, combinatorics, and its application in game theory, focusing on penalty kick strategies.

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

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

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

Demonstrates dynamic programming to find the number of ways to make change using different coin denominations.

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