Having said that, it makes sense that, at the end of our $1600$ simulations, we select our action based on this criterion:Each time we want to put a stone on the board we will use $1600$ runs of the Yes you’re right except for one thing! So, when $T$ is small (which is the case when we approach the end of the game), we will select $N(s, a_i)$, as we have highlighted a bit earlier, is the number of times we choose the action $a_i$ when we were in state $s$. The Neural Network will also output a float in the range $(-1, 1)$ telling us how likely it thinks we will win or lose the game. To do that, let’s suppose that we are in a certain state that is Before we deal with this case, let’s simplify the equation $\ref{eq:6}$. Obviously all the details of this algorithm are available in my For the first simulation, no stone has been placed on the board game. The What I want to show you is that all the worst actions will only be selected a very few times. The first component is a Neural Network while the second component is the Monte Carlo Tree Search (MCTS) Since we have run $1600$ simulations, it means that:$N(s, a_1) = 200 \quad\quad N(s, a_2) = 750 \quad\quad N(s, a_3) = 650$Now, we can finally understand the $\sim$ symbol. That has already been done by DeepMind and this Artificial Intelligence was named Obviously no! Take a deep breath, digest everything you’ve just learned and once you feel you’re in for more adventure, then follow me!I hope, that, before you continue reading, you’ve already assimilated everything and that you have rested your neurons because the next part might be a bit complicated!Don’t worry about my mental health dude, my neurons can handle your explanations! Side note: My own implementation of the AlphaGo Zero A.I is available on my github. we have simulated a player putting a stone in position $80$), then our Tree will have, similarly to the previous iteration, expanded the nodes from the node $80$.Again, as I’ve mentioned previously, we need to backpropagate these values back to all the nodes we visited before the node expansion. What matters is that, Let’s quickly understand what is the purpose of this The below table presents the values we got for both of these situations:represent the probability that we are going to select an action over the others(s) (More on this latter). Shortly after they have released their research paper explaining how work their algorithm, they have released another paper called That’s it! Then we keep on iterating until we reach the $1600^{th}$ expansion. When we will run the $1600$ $v(s, a_i)=1$ if we win and $v(s, a_i) = -1$ otherwise.Now, the previous argument doesn’t hold anymore since $v(s, a_i)$ is not any random variable output by a untrained Neural Network. To run the program, they also have used They’ve also used other common tricks. Nothing really tells us whether or not the data generated is a good approximation of the During the self-plays game, we generate data using the algorithm from Well, first of all, we need to be aware that, even though we generate data by using our Neural Network, the data generated is not the data that is Where $C$ is the size of the go board game ($C = 9$ or $C = 19$ usually). The AlphaGo Zero AI relies on 2 main components.
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