Courses of Study 2021-2022 
    Jul 06, 2022  
Courses of Study 2021-2022 [ARCHIVED CATALOG]

Add to Favorites (opens a new window)

CS 5789 - Introduction to Reinforcement Learning

Spring. 3 credits. Letter grades only.

Prerequisite: CS 5780 . Co-meets with CS 4789 .


Reinforcement Learning is one of the most popular paradigms for modelling interactive learning and sequential decision making in dynamical environments. This course introduces the basics of Reinforcement Learning and Markov Decision Process. The course will cover algorithms for planning and learning in Markov Decision Processes. We will discuss potential applications of Reinforcement Learning and their implications. We will study and implement classic Reinforcement Learning algorithms.

Outcome 1: Identify the differences between Reinforcement Learning and traditional Supervised Learning and grasp the key definitions of Markov Decision Processes.

Outcome 2: Analyze the performance of the class planning algorithms and learning algorithms for Markov Decision Process.

Outcome 3: Implement classic algorithms and demonstrate their performance on benchmarks.

Add to Favorites (opens a new window)