- Schedule of Classes - June 10, 2022 7:44AM EDT
- Course Catalog - June 9, 2022 7:14PM EDT
Course information provided by the Courses of Study 2021-2022.
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 the 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.
When Offered Spring.
Prerequisites/Corequisites Prerequisite: CS 4780.
- Identify the differences between Reinforcement Learning and traditional Supervised Learning and grasp the key definitions of Markov Decision Processes.
- Analyze the performance of the class planning algorithms and learning algorithms for Markov Decision Process.
- Implement classic algorithms and demonstrate their performance on benchmarks.
Regular Academic Session. Combined with: CS 5789
Credits and Grading Basis
3 Credits Graded(Letter grades only)
Class Number & Section Details
- MW Hollister Hall 110
- Jan 24 - May 10, 2022
Instruction Mode: In Person
Enrollment limited to CIS students only. All others should add themselves to the waitlist during add/drop in January.
Disabled for this roster.