Research Post

Memory-Augmented Monte Carlo Tree Search

This paper proposes and evaluates Memory-Augmented Monte Carlo Tree Search (M-MCTS), which provides a new approach to exploit generalization in online real-time search. The key idea of M-MCTS is to incorporate MCTS with a memory structure, where each entry contains information of a particular state. This memory is used to generate an approximate value estimation by combining the estimations of similar states. We show that the memory based value approximation is better than the vanilla Monte Carlo estimation with high probability under mild conditions. We evaluate M-MCTS in the game of Go. Experimental results show that M-MCTS outperforms the original MCTS with the same number of simulations.


The authors wish to thank Andrew Jacobsen for providing source code of Fuego with the neural network, and the anonymous referees for their valuable advice. This research was supported by NSERC, the Natural Sciences and Engineering Research Council of Canada.

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