تقرير
Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules
| العنوان: | Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules |
|---|---|
| المؤلفون: | Gonzalez, Johor Jara, Cooper, Seth, Guzdial, Matthew |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | 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. Comment: 10 pages, 6 figures, Artificial Intelligence and Interactive Digital Entertainment |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2309.09476 |
| رقم الأكسشن: | edsarx.2309.09476 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |