Re: [Computer-go] action-value Q for unexpanded nodes

I made a pull request to Leela, and put some data in there. It shows the details of how Q is initialized are actually important: https://github.com/gcp/leela-zero/pull/238 2017-12-03 19:56 GMT-06:00 Álvaro Begué : > You are asking about the selection of the move that

Re: [Computer-go] action-value Q for unexpanded nodes

Álvaro, you are quoting from "Expand and evaluate (Figure 2b)". But my question is about the section before that "Select (Figure 2a)". So the node has not been expanded+initialized. As Brian Lee mentioned, his MuGo uses the parent's value, which assumes without further information the value

Re: [Computer-go] action-value Q for unexpanded nodes

s,a) > visits. But in this case N(s,a)=0 so that won't work. > > Does anyone know how this is supposed to work? Or is it another detail AGZ > didn't spell out? > -- next part -- > An HTML attachment was scrubbed... > URL: < > http://computer-go.org/p

Re: [Computer-go] action-value Q for unexpanded nodes

The text in the appendix has the answer, in a paragraph titled "Expand and evaluate (Fig. 2b)": "[...] The leaf node is expanded and and each edge (s_t, a) is initialized to {N(s_t, a) = 0, W(s_t, a) = 0, Q(s_t, a) = 0, P(s_t, a) = p_a}; [...]" On Sun, Dec 3, 2017 at 11:27 AM, Andy

Re: [Computer-go] action-value Q for unexpanded nodes

They have a Q(s,a) term in their node-selection formula, but they don't tell what value they give to an action that has not yet been visited. Maybe Aja can tell us. - Mail original - De: "Álvaro Begué" À: "computer-go" Envoyé:

Re: [Computer-go] action-value Q for unexpanded nodes

Figure 2a shows two bolded Q+U max values. The second one is going to a leaf that doesn't exist yet, i.e. not expanded yet. Where do they get that Q value from? The associated text doesn't clarify the situation: "Figure 2: Monte-Carlo tree search in AlphaGo Zero. a Each simulation traverses the

Re: [Computer-go] action-value Q for unexpanded nodes

I am not sure where in the paper you think they use Q(s,a) for a node s that hasn't been expanded yet. Q(s,a) is a property of an edge of the graph. At a leaf they only use the `value' output of the neural network. If this doesn't match your understanding of the paper, please point to the

[Computer-go] action-value Q for unexpanded nodes

I don't see the AGZ paper explain what the mean action-value Q(s,a) should be for a node that hasn't been expanded yet. The equation for Q(s,a) has the term 1/N(s,a) in it because it's supposed to average over N(s,a) visits. But in this case N(s,a)=0 so that won't work. Does anyone know how this

Re: [Computer-go] Significance of resignation in AGZ

I have been interested in a different approach, and it had some elements in common with AGZ, so AGZ gave me the confidence to try it. From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of Chaz G. Sent: Sunday, December 3, 2017 4:05 AM To: computer-go@computer-go.org

[Computer-go] What happens if you only feed the current board position to AGZ?

AlphaGo Zero's Neural Network takes a 19x19x17 input representing the current and 15 previous board positons, and the side to play. What if you were to only give it the current board position and side to play, and you handled all illegal ko moves only in the tree? So obviously the network cannot

Re: [Computer-go] Significance of resignation in AGZ

Hi Brian, Thanks for sharing your genuinely interesting result. One question though: why would you train on a non-"zero" program? Do you think your program as a result of your rules would perform better than zero, or is it imitating the best known algorithm inconvenient for your purposes? Best,