Optimisation of MCTS Player for The Lord of the Rings: The Card Game

Godlewski, Konrad; Sawicki, Bartosz

doi:10.48550/arxiv.2109.12001

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2022

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“…Experiment with different 'weights' of heavy playouts are described by Godlewski and Sawicki (2021). Increasing the amount of expert knowledge added to the playout function generally improves the performance of the AI agent.…”

Section: Heavy Playoutsmentioning

confidence: 99%

Monte Carlo Tree Search: a review of recent modifications and applications

Świechowski¹,

et al. 2022

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Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games. However, in more complex games (e.g. those with a high branching factor or real-time ones) as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey was published in 2012. Contributions that appeared since its release are of particular interest for this review.

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Section: Heavy Playoutsmentioning

confidence: 99%