CS 258: INTRODUCTION TO REINFORCEMENT LEARNING
University of California, Riverside
4 Units, Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): EE 215 or EE 244 or CS 224 or EE 228 or CS 228 or EE 251B or CS 252B; graduate standing; or consent of instructor. This course introduces key ideas and algorithms of reinforcement learning (RL). Key topics covered include finite Markov Decision Process (MDP), dynamic programming, Monte Carlo methods, temporal-difference learning, policy gradient methods, safety-constrained RL, batch-constrained RL, multi-agent RL, multi-armed bandits, and imitation learning. May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor. Cross-listed with EE 227.
Average GPA: 3.81
Grade distribution records: 55 students across 2 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 4 | 7.3% |
| A | 30 | 54.5% |
| A- | 16 | 29.1% |
| B+ | 3 | 5.5% |
| C+ | 1 | 1.8% |
| C | 1 | 1.8% |
Based on 55 student grade records across 2 terms and 2 professors.
Instructors
- Nanpeng Yu 35 students, Average GPA 3.81
- Ioannis Karamouzas 20 students, Average GPA 3.80