In this work we investigate whether it is plausible to use the performance of a reinforcement learning (RL) agent to estimate the difficulty measured as the player completion rate of different levels in the mobile puzzle game Lily's Garden.For this purpose we train an RL agent and measure the number of moves required to complete a level. This is then compared to the level completion rate of a large sample of real players.We find that the strongest predictor of player completion rate for a level is the number of moves taken to complete a level of the ~5% best runs of the agent on a given level. A very interesting observation is that, while in absolute terms, the agent is unable to reach human-level performance across all levels, the differences in terms of behaviour between levels are highly correlated to the differences in human behaviour. Thus, despite performing sub-par, it is still possible to use the performance of the agent to estimate, and perhaps further model, player metrics.

翻译：本研究探讨了利用强化学习代理的性能来估计手机益智游戏《莉莉的花园》中不同关卡难度（以玩家通关率衡量）的可行性。为此，我们训练了一个强化学习代理，并测量其完成每个关卡所需的步数，然后将此结果与大量真实玩家的关卡通关率进行对比。研究发现，对关卡玩家通关率最具预测性的指标是代理在给定关卡中约前5%最佳运行结果的完成步数。一个极有趣的发现是：尽管从绝对值上看，代理在所有关卡上的表现均无法达到人类水平，但其在不同关卡间的行为差异与人类行为差异高度相关。因此，即使代理表现欠佳，我们仍可利用其性能来估算甚至进一步模拟玩家指标。