This post is related to a comment Alvaro made in a thread about AI in games of perfect information.
I'm interested in implementing Monte Carlo Tree search for a game that involves random outcomes. For some moves the outcome is fixed. For others there could be 6 or more different outcomes depending on some unknown value. The probabilities of each outcome occuring can change as the game progresses-- in this way it is similar to Poker.
I understand MCTS in terms of growing the tree for moves with a fixed outcome, but I'm confused about nodes that have a random outcome. Do I try to expand these nodes, or do I simply use the node to as a starting point for a number of simulated games?
If they are expanded, how? Is each possible outcome explored as a separate node? Are the probability distributions stored, and the node essentially 'randomized' each time it is visited (which would totally mess up the sanity of child nodes?) I can see the value in expanding them (to a degree) but it seems extermely risky given the nature of UCB-- it may attach priority to a very unlikely outcome.
Any help would be very much appreciated. Thanks!
willhMember Since 03 Mar 2009
Offline Last Active Sep 19 2012 07:00 AM
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