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Pruning Stochastic Game Trees Using Neural Networks for Reduced Action Space Approximation

Author

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  • Tasos Papagiannis

    (Zografou Campus, School of Electrical & Computer Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Georgios Alexandridis

    (Zografou Campus, School of Electrical & Computer Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Andreas Stafylopatis

    (Zografou Campus, School of Electrical & Computer Engineering, National Technical University of Athens, 15780 Athens, Greece)

Abstract

Monte Carlo Tree Search has proved to be very efficient in the broad domain of Game AI, though it suffers from high dimensionality in cases of large branching factors. Several pruning techniques have been proposed to tackle this problem, most of which require explicit domain knowledge. In this study, an approach using neural networks to determine the number of actions to be pruned, depending on the iterations run and the total number of possible actions, is proposed. Multi-armed bandit simulations with the UCB1 formula are employed to generate suitable datasets for the networks’ training and a specifically designed process is followed to select the best combination of the number of iterations and actions for pruning. Two pruning Monte Carlo Tree Search variants are investigated, based on different actions’ expected rewards’ distributions, and they are evaluated in the collectible card game Hearthstone. The proposed technique improves the performance of the Monte Carlo Tree Search algorithm in different setups of computational limitations regarding the available number of tree search iterations and is significantly boosted when combined with supervised learning trained-state value predicting models.

Suggested Citation

  • Tasos Papagiannis & Georgios Alexandridis & Andreas Stafylopatis, 2022. "Pruning Stochastic Game Trees Using Neural Networks for Reduced Action Space Approximation," Mathematics, MDPI, vol. 10(9), pages 1-16, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1509-:d:807146
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    References listed on IDEAS

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    1. Julian Schrittwieser & Ioannis Antonoglou & Thomas Hubert & Karen Simonyan & Laurent Sifre & Simon Schmitt & Arthur Guez & Edward Lockhart & Demis Hassabis & Thore Graepel & Timothy Lillicrap & David , 2020. "Mastering Atari, Go, chess and shogi by planning with a learned model," Nature, Nature, vol. 588(7839), pages 604-609, December.
    2. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    3. Michael N. Katehakis & Arthur F. Veinott, 1987. "The Multi-Armed Bandit Problem: Decomposition and Computation," Mathematics of Operations Research, INFORMS, vol. 12(2), pages 262-268, May.
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