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Monte Carlo Tree Search evaluates positions with the help of a playout policy. If the playout policy evaluates a position wrong then there are cases where the tree search has difficulties to find the correct move due to the large search space. This paper explores adaptive playout policies which improve the playout policy during a tree search. With the help of policy gradient reinforcement learning...
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