@kkkkjjjj4517

7:24 Your explanation of MCTS is not correct. For one instance of simulation: It picks the top move recommended by the network (greedy) most of the time, with random moves some of the time (epsilon). Then it walks into that move and repeats the same. It does it to completion. Then it backs up and keeps track of win vs visit ratio for each state as shown in the picture. It repeats this whole process 1600 times. As it is performing these walkthroughs it trains the networks and updates the values. So eventually, the more often you see a state, it will statistically converge to optimal value. MCTS runs to completion, its not a depth pruning algorithm. Temporal Difference stops somewhere in the middle, this was not used in AGZ. MCTS algorithm is discussed by David Silver in his lecture #8 towards the end.

@shamimhussain396

We, humans, run simulations in our heads all the time because sometimes simple intuitions are not enough... So, I guess, it isn't surprising that inclusion of Monte Carlo Tree Search would always drastically improve performance no matter how good the value function estimates are, even with the help of deep learning... The question is how to search more efficiently and also how to build an efficient model...

@siddarthc7091

the transition 'dhkk' hits hard

@alonamaloh

I've been programming board game engines for 25 years and I've followed the development of CNNs to play go quite closely. This video is a really good description of the AlphaGo Zero paper, with very clear explanations. Well, the explanation of MCTS was completely wrong, but other than that this video was great. I'll make sure to check out more from this channel.

@noranta4

This is a valuable explanation, this channel is a great discovery

@bhargav7476

that's some giga chad jaw u have there

@SantoshGupta-jn1wn

You explanation skills are fantastic!  I like how he has an outline at the begging of his video, very simple thing yet very effective when it comes to teaching a subject, yet so few educational videos do that. 

If I were to figure out the paper by myself, it would have taken me personally ~2x longer. 

 Subscribed.

@zzewt

This is cool, but after the third random jumpscare sound I couldn't pay attention to what you were saying--all I could think about was when the next one would be.  Gave up halfway through since it was stressing me out

@augustopertence2804

Best explanation I found about AlphaGo Zero

@dankelly

Awesome explination!   (And, you're greenscreen work looks great!)

@alaad1009

Excellent video

@elishaishaal7958

Thank you! This is the one of the clearest and most concise explanations of any paper I've found thus far.

@Hyrtsi

Excellent explanation, thanks!! I'm going to make my own 9*9 alphago zero version

@Sl4ab

It's very clear, thank you! I can't wait to discover the other videos :)

@johnvonhorn2942

Xander, you look like "The Hoff" (David Hasslehoff) and that's a great look!

@arijit07

This is the best video regarding Alpha GO paper. Just Amazing !!!

@LOGICZOMBIE

GREAT WORK

@greysky1786

Thank you.

@railgunpat3170

wow, i see some mistakes and also I didn't watch to much of your videos, but i find this channel is definitely underrated

@clrajapaksha

You explained technical stuff very clearly. Thanks Arxiv Insights