Intelligent Agents: Characteristics and Applications

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Related lectures (29)

Reinforcement Learning for Pacman Log in to Mediaspace to watch this video

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

Static Games: Introduction to Game Theory Concepts Log in to Mediaspace to watch this video

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

Zero-Sum Games: Concepts and Applications Log in to Mediaspace to watch this video

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

Potential Games: Best-Response Dynamics and Equilibria Log in to Mediaspace to watch this video

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

Reinforcement Learning Basics Log in to Mediaspace to watch this video

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

Distributed Multiagent Systems: Coordination and Learning Log in to Mediaspace to watch this video

Explores coordination and learning in distributed multiagent systems, covering social laws, task exchange, constraint satisfaction, and coordination algorithms.

Safe Learning and Control Log in to Mediaspace to watch this video

Explores safe learning, control, multi-agent coordination, and Nash equilibrium convergence in intelligent systems.

Connect Four: Alpha-Beta Pruning and Monte-Carlo Tree Search Log in to Mediaspace to watch this video

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

Perceptual Robotics: Integrating Vision and Action Log in to Mediaspace to watch this video

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

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