# COS 435 / ECE 433: Introduction to Reinforcement Learning
old syllabus / old schedule / old Ed / old Gradescope
* **Q**: How do I join the class/waitlist?
* If you are unable to enroll on the registrar website, then we have likely hit the course cap. Please come to lecture and sign in. Students will be added to the class based on the number of lectures attended.
* **Q**: Can I audit the course? No, but you are welcome to attend the lectures if you are not enrolled in the course.
* **Lecture**: tbd.
* **Precepts**: tbd.
* **Office Hours**: tbd.
* Prerequisites:
* Intro to ML: COS 324, ECE 435 or equivalent
* Probability: ORF 309, or equivalent
* Linear Algebra
* **Questions?** Ask on Ed. We will not be using Canvas. Assignments will be submitted on Gradescope.
 _**Reinforcement learning (RL)** is a machine learning technique that teaches agents how to make decisions that lead to good outcomes. This course will introduce fundamental concepts, important RL algorithms, and key challenges (e.g., exploration and generalization). The course will also highlight applications of RL to real-world problems, including health care and molecular science. Assignments will entail implementation of RL algorithms and mathematical analysis of these algorithms. Students will complete an open-ended final group project._
### Assignments
We have provided both the assignment and the TeX file for use as a template. Unless explicitly specified, please type up your solutions using TeX and submit both your compiled PDF and finished .ipynb on Gradescope.
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### Solutions
Solutions will be posted after each assignment is due.
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### Course Staff
](assets/s25/ben.jpg height=150) ](https://www.cs.princeton.edu/~rpa/rpa-photo.jpg height=150)
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