Model-based versus Model-free Reinforcement Learning

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Intelligent Agents: Making Decisions and Planning

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Multiagent Systems: Planning and Coordination

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Safe Learning for Autonomous Systems

Explores challenges in control, safety, and coordination for autonomous systems like autonomous cars, focusing on safe learning and Nash equilibria.

Reinforcement Learning Basics

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

Multi-Agent Systems: Traffic Management and Pricing Strategies

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

Reinforcement Learning: Q-Learning

Covers Q-Learning in reinforcement learning, exploring action values, policies, and the societal impact of algorithms.

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.

Learning Agents: Exploration-Exploitation Tradeoff

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Markov Games: Concepts and Applications in Reinforcement Learning

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Reinforcement Learning: Q-Learning

Covers Q-Learning, a model-free reinforcement learning algorithm, and its application to Tic-Tac-Toe with examples and quizzes.