[Computer-go] action-value Q for unexpanded nodes
Andy
andy.olsen.tx at gmail.com
Sun Dec 3 08:27:16 PST 2017
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.begue at 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.tx at 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?
>>
>>
>>
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>
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