Pretty close. Rave is accumulated for every trial (I don't want to throw away useful information). N depends on the board size. K > N and the ratio is not fixed. I use progressive widening, and I'm curious what the widening formula is for other programs, or how slowly it increases.
From: computer-go-boun...@computer-go.org [mailto:computer-go-boun...@computer-go.org] On Behalf Of Brian Sheppard Sent: Thursday, September 17, 2009 11:10 AM To: computer-go@computer-go.org Subject: [computer-go] rave and patterns Olivier and David both: a huge "thank you" for sharing your secrets. I think David makes clear that his large patterns apply only to the UCT process, and then only after a significant number of trials are reached. I gather that the lifecycle of a node is something like this in MFGO: 1) When a node is first created, search only the standard playout policy. 2) After N trials, begin accumulating RAVE statistics. (IIRC, David said that N = 4.) 3) On the K-th trial, use the Many Faces knowledge base to bias RAVE statistics. David, do I have this right? And is K > N? Or K >> N? In Pebbles, the process is slightly different: 1) When a node is first created, search only the standard playout policy. 2) RAVE data accumulates from the first trial. (Pebbles is slow. :-) ) 3) When that node has its first loss, then apply a progressive widening policy. In Pebbles, BTW, the progressive widening policy is rudimentary, consisting mostly of distance and 3x3 pattern. What is the lifecycle of a node in MoGo? Another question about MoGo: David makes clear that large patterns (and presumably joseki) apply only to the UCT process. Does MoGo apply such knowledge in playouts? Thanks again.
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