Sports betting is a popular past-time for many and a great use-case for an important concept known as dynamic programming that I’ll introduce in this video. We'll go over concepts like value iteration, the markov decision process, and the bellman optimality principle, all to help create a system that will help US optimally bet on the winning hockey team in order to maximize profits. Code, animations, theory, and yours truly. Enjoy!
Code for this video:
https://github.com/llSourcell/sports_betting_with_reinforcement_learning
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Github Syllabus:
https://github.com/llSourcell/Move_37_Syllabus
More learning resources:
https://artint.info/html/ArtInt_227.html
https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa
https://www.quora.com/What-is-an-intuitive-explanation-of-value-iteration-in-reinforcement-learning-RL
https://www.quora.com/How-is-policy-iteration-different-from-value-iteration
https://stackoverflow.com/questions/8337417/markov-decision-process-value-iteration-how-does-it-work
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