Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.
Explores the challenges and distinctions between human and artificial autonomy, touching on ethical implications and the conditions required for true autonomy.
Explores fundamental principles in scientific research, the impact of computers, numerical algorithms, and deep learning in solving high-dimensional problems.
Explores coordination and learning in distributed multiagent systems, covering social laws, task exchange, constraint satisfaction, and coordination algorithms.
