Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules
This work addresses the challenge of static approximators in automated game design, potentially improving rule generation for game developers, though it appears incremental by applying RL to an existing framework.
The paper tackles the problem of evaluating generated game rules in automated game design by using reinforcement learning as an approximator for human play, resulting in distinct rule sets compared to an A* agent baseline that may be more usable by humans.
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.