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+---
+Title: Reinforcement Learning
+Description: The study of optimally mapping situations to actions
+---
+
+# Reinforcement Learning
+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)
+
+