# COS 435 / ECE 433: Introduction to Reinforcement Learning
syllabus / schedule / Ed / Gradescope
* Waitlist form: [https://forms.gle/4hBk8NPkWaiWfk847](https://forms.gle/4hBk8NPkWaiWfk847). We're working on increasing the enrollment cap, and will email updates to everyone who has filled out the waitlist form.
* Lecture: Tuesday and Thursdays 11:00 -- 12:20 (room tbd)
* Precepts: Thur 1:30 - 2:20, Thur 3:30 - 4:20, Fri 11:00 -- 11:50
* Office Hours: tbd
* Prerequisites: linear algebra (e.g., COS302), machine learning (e.g., COS324, ECE435)
* Questions? Ask on [Ed](). See the instructors during class to get added to Ed.
![...](ideogram.jpeg width=200px) _**Reinforcement learning (RL)** is a core technology at the heart of modern intelligent systems that learn to make good decisions in complex environments. It encompasses technologies such as continuous variable optimization, Q learning, neural networks, policy search, and bandit exploration. In this course, we aim to give an introductory overview of reinforcement learning, its core challenges, and approaches, including exploration and generalization. In parallel, we will present a collection of case studies from intelligent systems, games and healthcare. Through a combination of lectures, written assignments and coding assignments, students will become well-versed in key ideas and techniques for RL._
### Course Staff
![[Mengdi Wang](https://mwang.princeton.edu/)](https://ece.princeton.edu/sites/g/files/toruqf1836/files/styles/3x4_750w_1000h/public/people/wang01.jpeg height=150) ![[Ben Eysenbach](https://ben-eysenbach.github.io/)](https://ben-eysenbach.github.io/assets/img/prof_pic.jpg height=150) ![TA 1](https://upload.wikimedia.org/wikipedia/commons/thumb/6/6e/Golde33443.jpg/220px-Golde33443.jpg height=150)