On Sat, Mar 26, 2011 at 7:54 AM, Erik van der Werf <[email protected] > wrote:
> It sounds like you're using a classical (deterministic) evaluation > function. > He is comparing it against a program that does classical evaluation, his MCTS version is not. That's how I understand what he is saying. I think the primary reason it's working so well in his checkers program is that he has no evaluation function in the version he is comparing against, other than counting material. But that's a big deal. The MCTS version is winning because the playouts give the program good strategy. Many years ago I experimented with checkers on a 6x6 board and found something similar. If I substituted playouts for an evaluation function, it played really strong compared to one with a piece only evaluation function. It may have played better than one WITH an evaluation function but it was so long ago I cannot remember. Don > Try combining UCT with Monte Carlo evaluation. > > Erik > > > On Sat, Mar 26, 2011 at 12:43 PM, Daniel Shawul <[email protected]> wrote: > > Hello, > > I am very new to UCT, just implemented basic UCT for go yesterday. > > But with no success so far for GO,I think mostly because it searches not > > very deep (depth = 3 on a 5 sec search with those values). > > I am using the following values as UCT parameters > > UCTK = sqrt(1/5) = 0.44 UCTN = 10 (visits afte which best move is > > expanded) > > Even if I lower UCTK down to 7 I get a maximum depth of d=7 at the start > > position for a 5 sec search. > > For how deep a search should I tune these parameter for ? > > Before UCT, I have an alpha-beta searcher which sometimes plays on CGOS. > > It reached a level of ~1500, and this engine seems to be too strong for > the > > UCT version. > > It just gets outsearched in some tactical positions and also in > evaluation > > I think. > > For example, I have an evaluation term which gives big bonuses for > connected > > strings which seems > > to give an edge in a lot of games.. How do you introduce such eval terms > in > > UCT ? > > But for my checkers program , to my big surprise , UCT made a significant > > impact. The regular > > alpha-beta searcher averages a depth=25 but the UCT version I think is > > equally strong from the games > > I saw. That was a kind of surprise for me because I thought UCT would > work > > better for bushy trees and > > when the eval has a lot of strategy. It also reached good depths > averaging > > 16 plies . > > My checkers eval had only material in it, so I don't know if UCT > is bringing > > strategy (distant information) to the game > > which the other one don't have.The games are not really played out to the > > end rather to a MAX_PLY = 96 > > afte which the material is counted and a WDL score is assigned (I call it > > partial playout). > > Also the fact that captures are forced seem to help a lot because it > doesn't > > make too many mistakes. > > I also found out some positions where it encounters similar problems as > > ladders in go. But in the checkers case, > > this problems are still solved correctly. Only problem is that it doesn't > > report correct looking winning rates. > > For example, in a position with two kings where one of the kings is > chasing > > the other to the sides to mate it, but > > the loosing king can draw by making a serious of correct moves to get > itself > > to one of the safe corners; The program > > displays winning rates of 0.01 (when it should have been more like 0.5) > but > > it still manages the draw ! > > thanks and apologies for the verbose email > > Daniel > > _______________________________________________ > > Computer-go mailing list > > [email protected] > > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go > > > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go >
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