Reinforcement Learning
Durham
Engineering&Physical Sciences :: Computer Science
Credits: 4.0
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Term: Fall 2024 - Full Term (08/26/2024 - 12/09/2024)
Grade Mode: Letter Grading
Class Size:
50
CRN: 16260
CRN: 16260
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.
Prerequisite(s): (CS 415 or CS 410P) and (MATH 539 or MATH 644)
Cross listed with : CS 851A.01
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 |