Just want to check what the expected playout performance is of well tuned monte-carlo engines? My MCTS engine is averaging apx 3,500 lightweight playouts per second on a single i5 32 bit cpu. Any suggestions on very efficient source code examples for fast monte-carlo playouts?
I've spent a lot of time comparing recursive group formation vs non-recursive but it doesn't seem to make a big difference. It seems that updating the list of likely moves after every play with something similar to the mogo probability rules is the most time consuming part as I currently recalculate the probabilities of moves at every empty point on the board each turn. It seems necessary if one doesn't want to handle all the exceptions to keeping the previous turn's play probabilities. Also any thoughts on combining pattern scoring and other conventional techniques together with a UCT tree? If two branches have very similar simulated win ratios could one use other factors to choose the best branch? It seems if there is a very wide branching such as at the beginning of the game, there is a lot of room to add other heuristics to choosing the best move when monte-carlo scores are within range of expected error. _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
