# MCTS Based Agents for Multistage Single-Player Card Game

arXiv: https://arxiv.org/abs/2109.12112 · HTML: https://mlsanity.com/paper/2109.12112/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms](https://mlsanity.com/paper/1808.04794/) (2018)

## Abstract (arXiv)

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.