Reinforcement Learning Chapter 2: Multi-Armed Bandits

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

AWESOME!!! Thank you so much!!

This is exactly what I was looking for, 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?

Wonderful tutorial, thanks :)

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.

doing the Coursera course then review with this is nice!

Great video. Thank you!

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

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~

For absolute beginners, perfect at 0.75x speed.

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

Brilliant!

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

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

good stuff, thank you!

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

thanks a lot for such wonderful explanations <3

These explanations are great. Thanks for making these videos!