# A Technique to Create Weaker Abstract Board Game Agents via Reinforcement Learning

arXiv: https://arxiv.org/abs/2209.00711 · HTML: https://mlsanity.com/paper/2209.00711/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Ludii -- The Ludemic General Game System](https://mlsanity.com/paper/1905.05013/) (2019)
- [Modern Techniques for Ancient Games](https://mlsanity.com/paper/2101.10066/) (2020)
- [Towards Game-Playing AI Benchmarks via Performance Reporting Standards](https://mlsanity.com/paper/2007.02742/) (2020)
- [Monte Carlo Q-learning for General Game Playing](https://mlsanity.com/paper/1802.05944/) (2018)
- [Calculating Ultra-Strong and Extended Solutions for Nine Men's Morris, Morabaraba, and Lasker Morris](https://mlsanity.com/paper/1408.0032/) (2014)

## Abstract (arXiv)

Board games, with the exception of solo games, need at least one other player to play. Because of this, we created Artificial Intelligent (AI) agents to play against us when an opponent is missing. These AI agents are created in a number of ways, but one challenge with these agents is that an agent can have superior ability compared to us. In this work, we describe how to create weaker AI agents that play board games. We use Tic-Tac-Toe, Nine-Men's Morris, and Mancala, and our technique uses a Reinforcement Learning model where an agent uses the Q-learning algorithm to learn these games. We show how these agents can learn to play the board game perfectly, and we then describe our approach to making weaker versions of these agents. Finally, we provide a methodology to compare AI agents.