Reinforcement learning

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Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.

Introduction to Game Theory

Covers game theory fundamentals, Nash equilibrium, and strategic interactions in multi-agent systems.

Deliberative Agents: Planning and Strategies

Covers planning with adversaries, heuristic search algorithms, and strategies for games with chance, emphasizing the significance of deliberative agents.

Intelligent Agents: Making Decisions and Planning

Covers intelligent agents, decision-making, planning, machine learning, and game theory.

Agent Platforms: Types, Intelligence, and Communication

Explores agent platforms, intelligence, and communication models in multiagent systems.

Multiagent Systems: Planning and Coordination

Explores the use of multi-agent systems for planning, coordination, and task assignment among heterogeneous agents.

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: Q-Learning

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

Safe Learning and Control

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

Alpha Go: Machine Learning and Game Strategies

Explores the historical perspective and development of the AlphaGo algorithm, focusing on machine learning and game strategies.

Autonomous Vehicles: Intelligence and Perception

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

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.

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.

Deep Learning Agents: Reinforcement Learning

Explores Deep Learning Agents in Reinforcement Learning, emphasizing neural network approximations and challenges in training multiagent systems.

Reinforcement Learning for Pacman

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

Agent Applications: Autonomous UAVs and MAS Interactions

Explores applications of autonomous agents in UAVs, air traffic management, and logistics, focusing on MAS interactions and adaptive transportation networks.

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

Virtual Humans: Creation and Applications

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

Game Theory: Stackelberg and Backward Induction

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