Reinforcement Learning
Durham
Engineering&Physical Sciences::Computer Science
Credits: 4.0
Class Size: 50
Term:
Fall 2024
-
Full Term (08/26/2024
-
12/09/2024)
CRN:
16260
Grade Mode:
Letter Grading
Reinforcement learning studies how agents can learn to act to achieve goals in complex, stochastic environments. This course introduces students to fundamental theoretical concepts of reinforcement learning, standard algorithms, and practical techniques. In addition to theoretical topics, the course
involves implementing basic algorithms in a high-level programming language.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Cross Listed With :
CS 851A (01)
Prerequisite(s): (CS 415 or CS 410P) and (MATH 539 or MATH 644)
Instructors:
Marek Petrik
Times & Locations
Start Date | End Date | Days | Time | Location |
---|---|---|---|---|
8/26/2024 | 12/9/2024 | TR | 9:40am - 11:00am | KING N101 |
Final Exam12/11/2024 | 12/11/2024 | W | 10:30am - 12:30pm | KING N101 |