MCTS Based Agents for Multistage Single-Player Card Game

TL;DR

This paper explores Monte Carlo Tree Search algorithms for a complex single-player card game, demonstrating that MCTS-based agents outperform expert rule-based agents through extensive simulations and strategy comparisons.

Contribution

Introduces MCTS algorithms tailored for a multi-stage card game, comparing different strategies and identifying optimal algorithm combinations for decision-making.

Findings

MCTS agents outperform expert rule-based agents.

Optimal combination of algorithms improves decision quality.

Different playout strategies impact performance significantly.

Abstract

The article presents the use of Monte Carlo Tree Search algorithms for the card game Lord of the Rings. The main challenge was the complexity of the game mechanics, in which each round consists of 5 decision stages and 2 random stages. To test various decision-making algorithms, a game simulator has been implemented. The research covered an agent based on expert rules, using flat Monte-Carlo search, as well as complete MCTS-UCB. Moreover different playout strategies has been compared. As a result of experiments, an optimal (assuming a limited time) combination of algorithms were formulated. The developed MCTS based method have demonstrated a advantage over agent with expert knowledge.