The key idea is to simulate many thousands of random games from the current po- sition, using self-play.
h, based on Monte-Carlo simulation, has revolutionised the performance of computer Go programs. In thi
is article we describe two extensions to the Monte-Carlo tree search algorithm
New positions are added into a search tree, and each node of the tree contains a value that predicts who will win from that position
updated by Monte-Carlo simulation:
alue of a node is simply the average out- come of all simulated games that visit the position
selecting the child node with the highest poten- tial value
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