Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning
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Publicado 2022-01-14
Citable link for this video: doi.org/10.52843/cassyni.ss11hp
This is a lecture in a series on reinforcement learning, following the new Chapter 11 from the 2nd edition of our book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz
Book Website: databookuw.com/
Book PDF: databookuw.com/databook.pdf
Amazon: www.amazon.com/Data-Driven-Science-Engineering-Lea…
Brunton Website: eigensteve.com
This video was produced at the University of Washington
Todos los comentarios (21)
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I personally love the big picture perspective that Prof. Brunton always shows. Please, continue to make these high quality videos!
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Thank you for the outstanding production quality and content of these lectures! I especially enjoy the structure diagram organizing the different RL methods.
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Thank you dear Prof Brunton for this outstanding lecture. The detailed explanations and focus on subtleties are so important , Looking forward to your next videos.
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Professor I must sincerely thank you for the astonishing quality of this video. You were able to clearly explain an advanced concept without simplifying, going into the details and providing brilliant insights. Also I sincerely thank you for saving my GPA from my R.L. exam 😆
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I enjoy your talks. They are very clear and well structured and have the right level of detail. Thank you,
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Prof. Burton, you are amazing. I never expected someone to take so much of time to explain a concept about TD. I'm one of the few people who hate reading text books to understand concepts. I rather see a video or learn about it from class. Thanks a lot
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Thank you so much for using very relevant analogies and very clear explanations. I think I have a much better grasp of the concepts behind Temporal Difference learning now.
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I was hoping that your next video would have been about Q-learning, and here it comes!
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Thank you! It's a great video. My understanding in TD learning was deepened a lot.
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CS PhD student here. This video provides such amazing content. Highly recommended.
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this was the best explanation ever! thank you so much, professor!
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Excellent class! Extremely easy to understand!
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These are fantastic lectures, I use these as an alternative explaination to David Silvers DeepmindxUCL 2015 lectures on the same topic, the different perspective really suits how my brain understands RL. Thank you!!
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Thank you for the clear picture. It was really well explained and others already mentioned, now I can say that I understand these techniques quite fairly well. 🙏
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Hi Prof. Brunton. Great vídeo as always! Please keep producing quality ML content
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Thanks a bundle Steve, this was really well explained!
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You gave the best explanations I've ever seen!
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I do like the description of Q Learning. I had come up with another analogy for why it makes sense. If you took the action of going out to a party, and then happened to make some mistakes while there, we wouldn't want to say "you should never go out again." We'd want to reinforce the action of going out based on the best* possible outcome of that night, not the suboptimal action that was taken once there.
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This is the best RL tutorial on the internet.
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The video quality is incredible lol and all the concept is discussed extremely clear OMG!! Brilliant masterpiece bro KEEP GOING !!
