Covers the characteristics, applications, and challenges of intelligent agents in software systems, emphasizing their role in making autonomous decisions and coordinating with other agents.
Explores applications of autonomous agents in UAVs, air traffic management, and logistics, focusing on MAS interactions and adaptive transportation networks.
Compares model-based and model-free reinforcement learning, highlighting the advantages of the former in adapting to reward changes and planning future actions.
By Professor David Bresch explores the origins and applications of natural catastrophe modeling, focusing on storms and their impact on risk mitigation and decision-making processes.
Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
