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Reinforcement Learning: Thompson Sampling & The Multi Armed Bandit Problem - Part 01
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- Опубликовано: 8 авг 2024
- Dr. Soper discusses reinforcement learning in the context of Thompson Sampling and the famous Multi-Armed Bandit Problem. Topics include what the multi-armed bandit problem is, why the multi-armed bandit problem is important, what Thompson Sampling is, how Thompson Sampling works, and the role of the beta distribution in Thompson Sampling.
Previous lesson (Foundations of Reinforcement Learning): • Foundations of Reinfor...
Next lesson (Reinforcement Learning: Thompson Sampling & The Multi Armed Bandit Problem - Part 02): • Reinforcement Learning...
At last someone explaining in simple terms.. Thank you.
Thanks, Dr. Soper. You are awesome. Your voice is soothing.
Thank you so so much Dr. Soper. Your content was very clear with exactly enough information to learn Thompson Sampling.
Thank you.. This was very clear and articulate delivery of the subject.
I finally understood the slot machine analogy haha, thanks so much Dr.Daniel Soper, look forward to more content from you x
Thank you for the time and the explanation. It was really clear !
thanks you Dr Soper!
concise and clear beautifully done!
Excellent Explainations !
Thank you very much. The shaded area at 14:16 is inaccurate. Only the left half of it means sampling from red distribution has bigger chances than blue.
Great video!
excellent presentation
Thank you so much for this!!
It was great, thank you
Well done
wow this is good. thank you Sir
This helped me alot thanks
well explained
finally, an immediately digestible explanation; thank you.
The best!
The multi-armed bandit problem is like the basic economic problem of unlimited wants exceeding limited resources, which results in scarcity, and thus, an opportunity cost when making a decision.
A 5 minute lecture crammed into 16 minutes. If you want to know how to implement Thompson sampling you won't find it in this video.
the principle of a good teacher is to make even those with difficulty understand what is being taught. Not everyone has prior knowledge of the subject, so, it's great that he explains it slowly.
very well explained