Intelligent agent

Applied sciences
Information engineering
Machine learning
Reinforcement learning

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Related lectures (32)

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Model-based versus Model-free Reinforcement Learning

Compares model-based and model-free reinforcement learning, highlighting the advantages of the former in adapting to reward changes and planning future actions.

Intelligent Agents: Characteristics and Applications

Covers the characteristics, applications, and challenges of intelligent agents in software systems, emphasizing their role in making autonomous decisions and coordinating with other agents.

Learning Agents: Exploration-Exploitation Tradeoff

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

Equilibrium State Prices Determination

Explains the determination of equilibrium state prices in asset pricing through consumption market clearing and budget constraints.

Autonomous Vehicles: Intelligence and Perception

Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.

Reactive Agents: Decision Processes

Explores reactive agents and decision processes, covering topics such as Markov Decision Processes and real-time agents.

Reinforcement Learning Basics

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

Markov Games: Concepts and Applications in Reinforcement Learning

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

Reinforcement Learning for Pacman

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

Agent Technology in Manufacturing: DaimlerChrysler Case Study

Explores DaimlerChrysler's use of agent technology in manufacturing, focusing on the Production 2000+ system and its benefits.