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101 lines
4.1 KiB
HTML
101 lines
4.1 KiB
HTML
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<h1>Reinforcement Learning</h1>
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<p>Reinforcement learning is the art of analyzing situations and mapping them to actions in order to maximize a numerical reward signal.</p>
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<p>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 <strong>make decisions</strong>.</p>
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<p>I have provided a list of topics that I wish to explore in a <a href="index.html%3Fresearch%252FReinforcementLearning%252Fsyllabus.html">syllabus</a></p>
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<h2>Readings</h2>
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<p>In order to spend more time learning, I decided to follow a textbook this time. </p>
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<p>Reinforcement Learning: An Introduction</p>
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<p>By Richard S. Sutton and Andrew G. Barto</p>
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<p><a href="index.html%3Fresearch%252FReinforcementLearning%252Freadings.html">Reading Schedule</a> </p>
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<h2>Notes</h2>
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<p>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.</p>
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<p><a href="index.html%3Fresearch%252FReinforcementLearning%252Fnotes.html">Notes page</a></p>
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<p>I wrote a small little quirky/funny report describing the bandit problem. Great for learning about the common considerations for Reinforcement Learning problems.</p>
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<p><a href="/files/research/TheBanditReport.pdf">The Bandit Report</a></p>
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<h2>Code</h2>
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<p>Code will occasionally be written to solidify the learning material and to act as aids for more exploration. </p>
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<p><a href="https://github.com/brandon-rozek/ReinforcementLearning">Github Link</a></p>
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<p>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</p>
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<p><a href="https://github.com/Brandon-Rozek/ReinforcementLearning/tree/master/agents">Github Link</a></p>
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