Playout policy adaptation with move features

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چکیده

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Playout policy adaptation with move features

Monte Carlo Tree Search (MCTS) is the state of the art algorithm for General Game Playing (GGP). We propose to learn a playout policy online so as to improve MCTS for GGP. We also propose to learn a policy not only using the moves but also according to the features of the moves. We test the resulting algorithms named Playout Policy Adaptation (PPA) and Playout Policy Adaptation with move Featur...

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Monte Carlo Tree Search (MCTS) is the state of the art algorithm for General Game Playing (GGP). Playout Policy Adaptation with move Features (PPAF) is a state of the art MCTS algorithm that learns a playout policy online. We propose a simple modification to PPAF consisting in memorizing the learned policy from one move to the next. We test PPAF with memorization (PPAFM) against PPAF and UCT fo...

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ژورنال

عنوان ژورنال: Theoretical Computer Science

سال: 2016

ISSN: 0304-3975

DOI: 10.1016/j.tcs.2016.06.024