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Learning Agents: Exploration-Exploitation Tradeoff

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

Reinforcement Learning: Basics and Applications

Covers the basics of reinforcement learning, including trial-and-error learning, Q-learning, deep RL, and applications in gaming and planning.

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.

Markov Games: Concepts and Applications in Reinforcement Learning

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

Distributed Multiagent Systems: Coordination and Learning

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

Reinforcement Learning: Q-Learning

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

Multi-Agent Systems: Traffic Management and Pricing Strategies

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

Game Theory Applications: Project Proposals and Examples

Provides guidance on project proposals in game theory, showcasing past examples and emphasizing problem formulation and mathematical modeling.