Course Number
525.637
Primary Program
Electrical and Computer Engineering
Location
Online
Mode of Study
Online

The course will provide a rigorous treatment of reinforcement learning by building on the mathematical foundations laid by optimal control, dynamic programming, and machine learning. Topics include model-based methods such as deterministic and stochastic dynamic programming, LQR and LQG control, as well as model-free methods that are broadly identified as Reinforcement Learning. In particular, we will cover on and off-policy tabular methods such as Monte Carlo, Temporal Differences, n-step bootstrapping, as well as approximate solution methods, including on- and off-policy approximation, policy gradient methods, including Deep Q-Learning. The course has a final project where students are expected to formulate and solve a problem based on the techniques learned in class.

Course Offering(s)

New
Open
First Time Online

Foundations of Reinforcement Learning

525.637.81
08/30/2021 - 12/14/2021
Semester
Fall 2021
Mode of Study
Online
Location
Online
Cost
$4,755
Textbook