  In strategy games, one of the most important aspects of game design is
maintaining a sense of challenge for players. Many mobile titles feature quick
gameplay loops that allow players to progress steadily, requiring an abundance
of levels and puzzles to prevent them from reaching the end too quickly. As
with any content creation, testing and validation are essential to ensure
engaging gameplay mechanics, enjoyable game assets, and playable levels. In
this paper, we propose an automated approach that can be leveraged for gameplay
testing and validation that combines traditional scripted methods with
reinforcement learning, reaping the benefits of both approaches while adapting
to new situations similarly to how a human player would. We test our solution
on a popular tower defense game, Plants vs. Zombies. The results show that
combining a learned approach, such as reinforcement learning, with a scripted
AI produces a higher-performing and more robust agent than using only heuristic
AI, achieving a 57.12% success rate compared to 47.95% in a set of 40 levels.
Moreover, the results demonstrate the difficulty of training a general agent
for this type of puzzle-like game.
