3 Credits
This course will focus on agents that must learn, plan, and act in complex, non-deterministic environments. We will cover the main theory and approaches of reinforcement learning (RL), along with common software libraries and packages used to implement and test RL algorithms. The course is a graduate seminar with assigned readings and discussions. The content of the course will be guided in part by the interests of the students. It will cover at least the first several chapters of the course textbook. Beyond that, we will move to more advanced and recent readings from the field (e.g., transfer learning and deep RL), with an aim towards focusing on the practical successes and challenges relating to reinforcement learning.