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Related lectures (31)

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Potential Games: Best Response Dynamics and Nash Equilibria

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

Reinforcement Learning Basics

Introduces the basics of reinforcement learning, including Q-learning and epsilon-greedy policies.

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.

Reinforcement Learning for Pacman

Explores applying reinforcement learning to teach Pacman to play autonomously using policy gradient methods and Markov decision processes.

Potential Games: Best-Response Dynamics and Equilibria

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

Perceptual Robotics: Integrating Vision and Action

Covers the integration of visual perception and robotic actions in embodied AI.

Virtual Humans: Creation and Applications

Delves into the creation of virtual humans, immersive VR systems, challenges in controlling digital facsimiles, and applications in medicine.

Reinforcement Learning for Pacman

Covers the application of reinforcement learning to teach Pacman to play autonomously by trial and error.

Game Theory: Stackelberg and Backward Induction

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

Reinforcement Learning: Basics

Covers the basics of reinforcement learning, including Q-learning and neural networks.