ECE 7202
Transcript Abbreviation:
ReinforceLearning
Course Description:
Fundamentals of Markov decision processes and reinforcement learning algorithms.
Course Levels:
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
Cross-listed in ISE.
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: Grad standing in Engineering or Math.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for ISE 7202.
Course Goals / Objectives:
Familiarize students with the framework of Markov decision processes
Introduce students to different classes of reinforcement learning algorithms
Help students gain experience in programming RL algorithms
Guide students through identifying research problems that can be addressed using RL methods
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Introduction to RL and applications | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Sequential decision making and multi-armed bandits | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Markov decision processes | 4.5 | 0.0 | 0 | 0.0 | 0 |
| Exact dynamic programming, value/policy iteration | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Reinforcement learning algorithms (including Monte Carlo and TD methods, Q-learning, policy gradient, actor-critic) | 21.0 | 0.0 | 0 | 0.0 | 0 |
| Selected advanced topics: multi-agent RL, inverse RL, on-policy vs off-policy, imitation learning, e | 4.5 | 0.0 | 0 | 0.0 | 0 |
| Total | 42 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 60% |
| Final Project | 40% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| No mandatory textbook required. Suggested references included in the syllabus. |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: