Reinforcement Learning Chapter 2: Multi-Armed Bandits

These explanations are great. Thanks for making these videos!

thanks a lot for such wonderful explanations <3

Thanks a lot for such a good explanation and your time!

good stuff, thank you!

two minutes in and the entire complex ball of RL seems much simpler. thank you. subbed

Thanks a lot for making this series! could you send me the link for these slides?

Brilliant!

Is the greedy action selection rule also termed as 'best action in hindsight' ?

For absolute beginners, perfect at 0.75x speed.

Excellent, your speech is very clear, and I'm curious about the slides presentation software in your video, could you tell me what the app you use? I just want to try it~

So no one really knows how to teach RL.......

Great video. Thank you!

doing the Coursera course then review with this is nice!

I know you mention it at least once, but could you define the variables you use in the equations at least once on the slides? Would be quite helpful.

Wonderful tutorial, thanks :)

at 6.16 I don't understand why the value change over time in greedy algorithm! even with small amount

shouldn't the value be the same because we don't discover at all which mean the same value repeated every timestep?

This is exactly what I was looking for, thanks!

AWESOME!!! Thank you so much!!

your explanations are good, but you speak too fast. non-american english speakers may have difficulty understanding you at few places.