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45 lines
1.6 KiB
Markdown
45 lines
1.6 KiB
Markdown
---
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Title: Reinforcement Learning
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Description: The study of optimally mapping situations to actions
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---
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# Reinforcement Learning
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Reinforcement learning is the art of analyzing situations and mapping them to actions in order to maximize a numerical reward signal.
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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**.
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I have provided a list of topics that I wish to explore in a [syllabus](syllabus)
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## Readings
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In order to spend more time learning, I decided to follow a textbook this time.
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Reinforcement Learning: An Introduction
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By Richard S. Sutton and Andrew G. Barto
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[Reading Schedule](readings)
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## Notes
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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.
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[Notes page](notes)
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I wrote a small little quirky/funny report describing the bandit problem. Great for learning about the common considerations for Reinforcement Learning problems.
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[The Bandit Report](/files/research/TheBanditReport.pdf)
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## Code
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Code will occasionally be written to solidify the learning material and to act as aids for more exploration.
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[Github Link](https://github.com/brandon-rozek/ReinforcementLearning)
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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
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[Github Link](https://github.com/Brandon-Rozek/ReinforcementLearning/tree/master/agents)
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