  Playtesting is an essential step in the game design process. Game designers
use the feedback from playtests to refine their designs. Game designers may
employ procedural personas to automate the playtesting process. In this paper,
we present two approaches to improve automated playtesting. First, we propose
developing persona, which allows a persona to progress to different goals. In
contrast, the procedural persona is fixed to a single goal. Second, a human
playtester knows which paths she has tested before, and during the consequent
tests, she may test different paths. However, Reinforcement Learning (RL)
agents disregard these previous paths. We propose a novel methodology that we
refer to as Alternative Path Finder (APF). We train APF with previous paths and
employ APF during the training of an RL agent. APF modulates the reward
structure of the environment while preserving the agent's goal. When evaluated,
the agent generates a different trajectory that achieves the same goal. We use
the General Video Game Artificial Intelligence (GVG-AI) and VizDoom frameworks
to test our proposed methodologies. We use Proximal Policy Optimization (PPO)
RL agent during experiments. First, we compare the playtest data generated by
developing and procedural persona. Our experiments show that developing persona
provides better insight into the game and how different players would play.
Second, we present the alternative paths found using APF and argue why
traditional RL agents cannot learn those paths.
