STATS 219 · STAT 219Topics in Reinforcement Learning
Statistics · 4 units · Graduate courses (200-299)
Designed for graduate students. Reinforcement learning (RL) provides the key to enabling machines to make intelligent decisions, learn from their experiences, and optimize their actions in dynamic and uncertain environments. From recommendation systems tailored to individual preferences to self-driving cars navigating complex traffic scenarios, RL is at the heart of enabling these remarkable feats. Study delves into the depth and breadth of RL, covering a wide range of topics from theoretical foundations to practical algorithmic implementations including multi-armed bandits, Markov decision process, and stochastic optimal control.
S/U or letter grading.
When it runs
- Fall 2026
Scheduled, not typical — from UCLA’s Schedule of Classes, which publishes Fall 2025 through Spring 2027 and nothing before it.
Requisites
Official UCLA wording
Requisite: course 200A.
BruinTree reads · Prerequisite
confidence 1.00 · from UCLA’s structured dataRequires
Everything that has to come before this course, not just the courses named in the requisite above.
STATS 219
- STATS 200AApplied Probability
- STATS 100AIntroduction to Probability
- MATH 170AProbability Theory I
1 direct requisite. Showing 8 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
No course in the catalog lists this as a requisite.