Deep Reinforcement Learning Applications

Catalog description

Deep Reinforce. Learning Appl. will cover advanced sequential decision-making topics in AI and will consist of two parts 1) deep reinforce. learning theory and 2) deep reinforce. learning applications. Deep reinforce. learning combines reinforce. learning and deep learning. The theory module will introduce students to major deep reinforce. learning algorithms, modeling process, and programming. The applications module will include case studies on the practical applications of deep reinforce. learning in industry. This is a project-based course with extensive Pytorch/Tensorflow hands-on exercises. Students will also have an opportunity to improve their GitHub profile by working on projects.