# Mastering Terra Mystica: Applying Self-Play to Multi-agent Cooperative Board Games

arXiv: https://arxiv.org/abs/2102.10540 · HTML: https://mlsanity.com/paper/2102.10540/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 21 Feb 2021.

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

- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)

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

- [Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks](https://mlsanity.com/paper/2502.13918/) (2025)

## Abstract (arXiv)

In this paper, we explore and compare multiple algorithms for solving the complex strategy game of Terra Mystica, hereafter abbreviated as TM. Previous work in the area of super-human game-play using AI has proven effective, with recent break-through for generic algorithms in games such as Go, Chess, and Shogi citeAlphaZero. We directly apply these breakthroughs to a novel state-representation of TM with the goal of creating an AI that will rival human players. Specifically, we present the initial results of applying AlphaZero to this state-representation and analyze the strategies developed. A brief analysis is presented. We call this modified algorithm with our novel state-representation AlphaTM. In the end, we discuss the success and shortcomings of this method by comparing against multiple baselines and typical human scores. All code used for this paper is available at on hrefhttps://github.com/kandluis/terrazeroGitHub.