Reinforcement Learning (RL) is the study of sequential decision making under uncertainty with applications abounding a vast range of real-world problems, from advertisements and recommendations systems to robotics and autonomous vehicles. In this course, we learn how to approach RL problems from both theoretical and empirical viewpoints. We study how RL agents interact with their surrounding environments and learn how to improve their behavior over time. After this course, we will be comfortable understanding how companies like Amazon, Yelp, Netflix, Adobe, and Google provide their recommendation services, and how companies like DeepMind can master video games such as Atari games and StarCraft.
The course assumes students are comfortable with basic concepts in analysis, elementary algebra, probability theory, statistics, and programming. It is recommended that students take courses in linear algebra, measure theory, learning theory, and deep learning, but it is not necessary. We will cover the required material in this course.
*Make sure you are enrolled in the course piazza.
*For information regarding auditing and enrolling contact Lori O’Brien.
*We do not use a particular textbook.
[HW1: 8%, HW2: 8%, HW3[midterm]: 20%, HW4: 8%, HW5: 8%, HW6: 8%, HW7[final]: 20%]
*No late submission would be accepted.
I encourage the students to discuss and exchange ideas for the problem sets and the project, but the students shall accomplish each of their problem sets and project separately. Make sure you do not violate the Honor Code. If you are discussing with other students but cannot be informative enough without consulting your solution, please do not pursue the discussion. Instead, refer your colleagues and classmates to a teaching assistant.
In case you observe symptoms or become quarantined or isolated at any point in time during the semester, immediately reach out to the Protect Purdue Health Center, Academic Case Manager, and the Office of the Dean of Students.