Upon successful completion of this course, the students should:
This course offers an in-depth study on both theoretical and algorithmic foundations of Deep Reinforcement Learning (DRL). The contents of the course are relevant for applications in multiple domains with complex decision-making tasks, such as robotic control, autonomous vehicles and navigation, resource allocation in wireless networks, optimizing inventory or industrial processes, resource management in computing clusters, traffic control, games, finance, medicine, personalized recommendations, bidding and advertising, web navigation, among others.
The course covers the following main topics:
Faculty of Engineering and Science