  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.
