REINFORCEMENT LEARNING

AI406

Course Type	Elective Course
Course Level	Bachelor's Degree
Year of Study	-
Semester	-
ECTS	5
Name of Lecturer(s)	-
Learning Outcomes	At the end of this course, the students;
Mode of Delivery
Prerequisites	No
Recommended Optional Programme Components
Course Description	To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will provide a solid introduction to the field of reinforcement learning and students will learn about the core challenges and approaches, including generalization and exploration. Through a combination of lectures, and written and coding assignments, students will become well versed in key ideas and techniques for RL. Assignments will include the basics of reinforcement learning as well as deep reinforcement learning ? an extremely promising new area that combines deep learning techniques with reinforcement learning.
Course Contents
Recommended or Required Reading	* Reinforcement Learning: An Introduction, Sutton and Barto, 2nd Edition * Reinforcement Learning: State-of-the-Art, Marco Wiering and Martijn van Otterlo
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Language of Instruction	-
Work Placement(s)	No