What you wrote sounds like you re-discovered the importance of progressive widening PW ;-)
(See: Coulom, Computing Elo Ratings of Move Patterns in the Game of Go, 4.2 Progressive Widening of the Monte-Carlo Search Tree) In 19x19 when I implemented (about 1 year ago) RAVE a progressive widening I had the first wins against gnugo in 19x19 (I had some 9x9 wins before). But the reason PW is so good is somewhat different when you combine it with RAVE: When the node has few visits, you only explore (say) 3 moves and those moves are the 3 best moves according to some a-priori heuristic, but when you widen the tree, you do NOT include the 4th move according to the same criterion, but the best non-explored RAVE candidate. Only 3 nodes are considered for UCT (in the beginning, of course) but ALL nodes get RAVE updates. And these RAVE updates are specific for the path in the tree leading to the node. So all non explored nodes get high quality RAVE information and when you widen the tree the 4th candidate is a good move for whole board position represented by the node. The way I implemented PW for the first time is the formula by Hiroshi Yamashita (below) Jacques. (I copy/paste from my notes. It is somewhere in the list.) Aya: ---- (1 - beta) * (win_rate + 0.31 * sqrt( ln(parent_visits) / child_visits)) + beta (rave_win_rate * 0.31 * sqrt( ln(rave_parent_visits) / rave_child_visits)) beta = sqrt(100 / (3 * child_visits + 100)); Aya uses Progressive Widening. High order N moves are only considerd. PW_sort_N = ln(parent_visits/ 40.0) / ln(1.4) +2; Moves are sorted sometimes by rave value, Criticality, and MC owners. I also would like to know how to count rave. UCT searches B(E5),W(D3),B(C5),W(F7), and in this position, playout searches B(E7),W(E8),B(D8),W(F8),B(D7).. Black win. In W(D3) positions, Aya updates RAVE and UCT, Updates C5(UCT) Updates C5(RAVE) Updates E7(RAVE) Updates D8(RAVE) Updates D7(RAVE) I think "Updates C5(RAVE)" is strange, but I could not get good result without this. Hiroshi Yamashita
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