On successful completion of this course, the student should:
This course will teach the foundations of applied reinforcement learning and deep reinforcement learning. They will, at course completion, be able to build and train stateof-the-art reinforcement learning algorithms, understand the importance of hyperparameter tuning, and understand when to apply different algorithms. The students will also learn the theoretical foundation of reinforcement learning and concepts used to improve the performance of reinforcement learning algorithms, including deep neural networks.
The course adapts to the state-of-the-art literature and covers theoretical and practical topics of reinforcement learning research. These topics include:
Combination of lectures, assignments, paper studies, lab, report writing, and self-study. The tasks are done individually or in small groups of 2 students with group supervision.
The workload for the average student is approximately 200 hours.
Graded portfolio assessment, individually or in groups. Groups are given joint grades.
Information about the portfolio content will be given in Canvas by the start of the semester.
1 semester
7.5
Spring
Grimstad
Faculty of Engineering and Science