Playing Carcassonne with Monte Carlo Tree Search
Abstract
Monte Carlo Tree Search (MCTS) is a relatively new sampling method with multiple variants in the literature. They can be applied to a wide variety of challenging domains including board games, video games, and energy-based problems to mention a few. In this work, we explore the use of the vanilla MCTS and the MCTS with Rapid Action Value Estimation (MCTS-RAVE) in the game of Carcassonne, a stochastic game with a deceptive scoring system where limited research has been conducted. We compare the strengths of the MCTS-based methods with the Star2.5 algorithm, previously reported to yield competitive results in the game of Carcassonne when a domain-specific heuristic is used to evaluate the game states. We analyse the particularities of the strategies adopted by the algorithms when they share a common reward system. The MCTS-based methods consistently outperformed the Star2.5 algorithm given their ability to find and follow long-term strategies, with the vanilla MCTS exhibiting a more robust game-play than the MCTS-RAVE.
Keywords
Cite
@article{arxiv.2009.12974,
title = {Playing Carcassonne with Monte Carlo Tree Search},
author = {Fred Valdez Ameneyro and Edgar Galvan and Anger Fernando Kuri Morales},
journal= {arXiv preprint arXiv:2009.12974},
year = {2020}
}
Comments
8 pages, 6 figures