Introduction (Move 37)

Welcome to my new reinforcement learning course titled Move 37! in this 10 week course we'll go over the basics of reinforcement learning up to modern day techniques that involve neural networks called 'deep' reinforcement learning. In this first video, i'll introduce the idea of a Markov Decision Process. This is the basic mathematical framework for framing the reinforcement learning problem. We'll also briefly mention the ideas of a 'policy' and the agent-environment loop. Get hype!

Code for this video:
https://github.com/llSourcell/Introduction_Move37

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Github Syllabus:
https://github.com/llSourcell/Move_37_Syllabus

More learning resources:
https://towardsdatascience.com/reinforcement-learning-demystified-markov-decision-processes-part-1-bf00dda41690
https://www.cs.rice.edu/~vardi/dag01/givan1.pdf
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-825-techniques-in-artificial-intelligence-sma-5504-fall-2002/lecture-notes/Lecture20FinalPart1.pdf
https://artint.info/html/ArtInt_224.html

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Видео Introduction (Move 37) канала Siraj Raval