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 should be close to the same as before. LeelaZ uses 1.1 for a "first play urgency", which assumes you should prioritize getting at least one evaluation from the NN for each node. https://github.com/gcp/leela-zero/blob/master/src/UCTNode.cpp#L323 Finally using a value of 0 would seem to place extra confidence in the policy net values. I feel like MuGo's implementation makes sense, but I'm trying to get some experimental evidence showing the impact before suggesting it to Leela's author. So far my self-play tests with different settings do not show a big impact, but I am changing other variables at the same time. - Andy 2017-12-03 14:30 GMT-06:00 Álvaro Begué <alvaro.be...@gmail.com>: > 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 <andy.olsen...@gmail.com> wrote: > >> 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 tree by >> selecting the edge with maximum action-value Q, plus an upper confidence >> bound U that depends on a stored prior probability P and visit count N for >> that edge (which is incremented once traversed). b The leaf node is >> expanded..." >> >> >> >> >> >> >> 2017-12-03 9:44 GMT-06:00 Álvaro Begué <alvaro.be...@gmail.com>: >> >>> 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 specific paragraph that you are having trouble with. >>> >>> Álvaro. >>> >>> >>> >>> On Sun, Dec 3, 2017 at 9:53 AM, Andy <andy.olsen...@gmail.com> wrote: >>> >>>> 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 is supposed to work? Or is it another detail >>>> AGZ didn't spell out? >>>> >>>> >>>> >>>> _______________________________________________ >>>> Computer-go mailing list >>>> Computer-go@computer-go.org >>>> http://computer-go.org/mailman/listinfo/computer-go >>>> >>> >>> >>> _______________________________________________ >>> Computer-go mailing list >>> Computer-go@computer-go.org >>> http://computer-go.org/mailman/listinfo/computer-go >>> >> >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> > > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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