Markov Games: Concepts and Applications in Reinforcement Learning

Related lectures (32)

Discusses Markov Decision Processes and dynamic programming techniques for solving optimal policies in various scenarios.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Explores solving Connect Four using game theory algorithms and compares Alpha-Beta pruning with Monte-Carlo tree search.

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

Covers decision-making in marketing based on customer behavior for optimal strategies.

Delves into learning dynamics in games, exploring chaos, limit cycles, and the interplay between convergence and chaos.

Explores primal-dual optimization algorithms for convex-concave minimax problems, discussing convergence properties and applications.

Covers bidding strategies, Game Theory, Nash equilibrium, system security, ancillary services, and market power in power systems.

Explores the Asset Selling Problem to maximize long-term reward without a deadline.

Explores the analysis of trust regions with Cauchy steps in optimization.

Covers the convergence of fixed point methods for nonlinear equations, including global and local convergence theorems and the order of convergence.

Covers the linear programming approach to reinforcement learning, focusing on its applications and advantages in solving Markov decision processes.

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