# Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules

arXiv: https://arxiv.org/abs/2309.09476 · HTML: https://mlsanity.com/paper/2309.09476/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [PCGRL: Procedural Content Generation via Reinforcement Learning](https://mlsanity.com/paper/2001.09212/) (2020)
- [Baba is Y'all: Collaborative Mixed-Initiative Level Design](https://mlsanity.com/paper/2003.14294/) (2020)
- [Automated Game Design via Conceptual Expansion](https://mlsanity.com/paper/1809.02232/) (2018)
- [Conceptual Game Expansion](https://mlsanity.com/paper/2002.09636/) (2020)
- [Automated Play-Testing Through RL Based Human-Like Play-Styles Generation](https://mlsanity.com/paper/2211.17188/) (2022)
- [Evaluation of a Recommender System for Assisting Novice Game Designers](https://mlsanity.com/paper/1908.04629/) (2019)
- [Generating Real-Time Strategy Game Units Using Search-Based Procedural Content Generation and Monte Carlo Tree Search](https://mlsanity.com/paper/2212.03387/) (2022)

## Built on by (most cited first, 4 of 4)

- [Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis](https://mlsanity.com/paper/2410.01096/) (2024)
- [ScriptDoctor: Automatic Generation of PuzzleScript Games via Large Language Models and Tree Search](https://mlsanity.com/paper/2506.06524/) (2025)
- [Mortar: Evolving Mechanics for Automatic Game Design](https://mlsanity.com/paper/2601.00105/) (2025)
- [An Exploration of Collision-based Enemy Morphology Generation](https://mlsanity.com/paper/2606.02832/) (2026)

## Abstract (arXiv)

Automated game design (AGD), the study of automatically generating game rules, has a long history in technical games research. AGD approaches generally rely on approximations of human play, either objective functions or AI agents. Despite this, the majority of these approximators are static, meaning they do not reflect human player's ability to learn and improve in a game. In this paper, we investigate the application of Reinforcement Learning (RL) as an approximator for human play for rule generation. We recreate the classic AGD environment Mechanic Maker in Unity as a new, open-source rule generation framework. Our results demonstrate that RL produces distinct sets of rules from an A* agent baseline, which may be more usable by humans.