Abstract
In this paper, we propose a variation of the MCTS framework to perform a search in several trees to exploit game decompositions. Our Multiple Tree MCTS (MT-MCTS) approach builds simultaneously multiple MCTS trees corresponding to the different sub-games and allows, like MCTS algorithms, to evaluate moves while playing. We apply MT-MCTS on decomposed games in the General Game Playing framework. We present encouraging results on single player games showing that this approach is promising and opens new avenues for further research in the domain of decomposition exploitation. Complex compound games are solved from 2 times faster (Incredible) up to 25 times faster (Nonogram).
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Notes
- 1.
In the GGP framework, player moves are always simultaneous. In alternate move games, all players but one play a useless move to skip the turn.
- 2.
A decomposition is partial if a sub-game can be further decomposed.
- 3.
An informal presentation of MT-MCTS with an example has been published in Journées d’Intelligence Artificielle Fondamentale (JIAF) 2019.
- 4.
The experiments are performed on one core of an Intel Core i7 2,7 GHz with 8Go of 1.6 GHz DDR3.
- 5.
As each playout results in an expansion of the tree, we can compare the number of playouts with the number of calculated states.
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Hufschmitt, A., Vittaut, JN., Jouandeau, N. (2020). Exploiting Game Decompositions in Monte Carlo Tree Search. In: Cazenave, T., van den Herik, J., Saffidine, A., Wu, IC. (eds) Advances in Computer Games. ACG 2019. Lecture Notes in Computer Science(), vol 12516. Springer, Cham. https://doi.org/10.1007/978-3-030-65883-0_9
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