Course Websites
CS 443 - Reinforcement Learning
Last offered Spring 2024
Official Description
Fundamental concepts and basic algorithms in Reinforcement Learning (RL) - a machine learning paradigm for sequential decision-making. The goal of this course is to enable students to (1) understand the mathematical framework of RL, (2) tell what problems can be solved with RL, and how to cast these problems into the RL formulation, (3) understand why and how RL algorithms are designed to work, and (4) know how to experimentally and mathematically evaluate the effectiveness of an RL algorithm. There will be both programming and written assignments. Course Information: 3 undergraduate hours. 4 graduate hours. Prerequisite: CS 225; MATH 241; one of MATH 225, MATH 257, MATH 415, MATH 416, ASRM 406 or BIOE 210; one of CS 361, STAT 361, ECE 313, MATH 362, MATH 461, MATH 463 or STAT 400.
Related Faculty
Schedule and Instructors
| Title | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|
| Reinforcement Learning | CSP | 76232 | PKG | 3 | - | Nan Jiang | ||
| Reinforcement Learning | CSP | 76232 | PKG | 3 | - | Nan Jiang | ||
| Reinforcement Learning | MCS | 76233 | ONL | 4 | - | Nan Jiang | ||
| Reinforcement Learning | RLG | 74872 | LEC | 4 | 1400 - 1515 | T R | 1306 Everitt Laboratory | Nan Jiang |
| Reinforcement Learning | RLU | 74871 | LEC | 3 | 1400 - 1515 | T R | 1306 Everitt Laboratory | Nan Jiang |