website/content/research/reinforcementlearning.md
2023-09-26 17:43:41 -04:00

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---
Title: Reinforcement Learning
Description: The study of optimally mapping situations to actions
---
Reinforcement learning is the art of analyzing situations and mapping them to actions in order to maximize a numerical reward signal.
In this independent study, I as well as Dr. Stephen Davies, will explore the Reinforcement Learning problem and its subproblems. We will go over the bandit problem, markov decision processes, and discover how best to translate a problem in order to **make decisions**.
I have provided a list of topics that I wish to explore in a [syllabus](syllabus/)
## Readings
In order to spend more time learning, I decided to follow a textbook this time.
Reinforcement Learning: An Introduction
By Richard S. Sutton and Andrew G. Barto
[Reading Schedule](readings/)
## Notes
The notes for this course, is going to be an extreemly summarized version of the textbook. There will also be notes on whatever side tangents Dr. Davies and I explore.
[Notes page](notes/)
I wrote a small little quirky/funny report describing the bandit problem. Great for learning about the common considerations for Reinforcement Learning problems.
[The Bandit Report](/files/research/TheBanditReport.pdf)
## Code
Code will occasionally be written to solidify the learning material and to act as aids for more exploration.
[Github Link](https://github.com/brandon-rozek/ReinforcementLearning)
Specifically, if you want to see agents I've created to solve some OpenAI environments, take a look at this specific folder in the Github Repository. [Github Link](https://github.com/Brandon-Rozek/ReinforcementLearning/tree/master/agents)