On 22-05-17 15:46, Erik van der Werf wrote: > Oh, haha, after reading Brian's post I guess I misunderstood :-) > > Anyway, LMR seems like a good idea, but last time I tried it (in Migos) > it did not help. In Magog I had some good results with fractional depth > reductions (like in Realization Probability Search), but it's a long > time ago and the engines were much weaker then...
What was generating your probabilities, though? A strong policy DCNN or something weaker? ERPS (LMR with fractional reductions based on move probabilities) with alpha-beta seems very similar to having MCTS with the policy prior being a factor in the UCT formula. This is what AlphaGo did according to their 2015 paper, so it can't be terrible, but it does mean that you are 100% blind to something the policy network doesn't see, which seems worrisome. I think I asked Aja once about what they do with first play urgency given that the paper doesn't address it - he politely ignored the question :-) The obvious defense (when looking at it in alpha-beta formulation) would be to cap the depth reduction, and (in MCTS/UCT formulation) to cap the minimum probability. I had no success with this in Go so far. -- GCP _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
