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

arXiv: https://arxiv.org/abs/2109.12001 · HTML: https://mlsanity.com/paper/2109.12001/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 1 of 1)

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

## Built on by (most cited first, 1 of 1)

- [Two-Step Reinforcement Learning for Multistage Strategy Card Game](https://mlsanity.com/paper/2311.17305/) (2023)

## Abstract (arXiv)

The article presents research on the use of Monte-Carlo Tree Search (MCTS) methods to create an artificial player for the popular card game "The Lord of the Rings". The game is characterized by complicated rules, multi-stage round construction, and a high level of randomness. The described study found that the best probability of a win is received for a strategy combining expert knowledge-based agents with MCTS agents at different decision stages. It is also beneficial to replace random playouts with playouts using expert knowledge. The results of the final experiments indicate that the relative effectiveness of the developed solution grows as the difficulty of the game increases.