CS 4789 - Introduction to Reinforcement Learning

Spring. 3 credits. Letter grades only.

Prerequisite: CS 4780.

W. Sun.

Reinforcement Learning is one of the most popular paradigms for modelling interactive learning and sequential decision making. This course introduces the basics of Reinforcement Learning. The course will cover basics of Markov Decision Process, Planning and Learning in Markov Decision Processes. We will discuss potential applications of Reinforcement Learning. 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.