论文标题
没有玩家预测游戏困难和流失
Predicting Game Difficulty and Churn Without Players
论文作者
论文摘要
我们提出了一个新颖的模拟模型，该模型能够预测流行的移动免费游戏的愤怒小鸟梦想爆炸的每级流失和通过。我们的主要贡献是使用深入加强学习（DRL）与玩家人口如何发展水平的模拟结合AI游戏玩法。 AI玩家预测了水平难度，该水平难度用于以模拟技能，持久性和无聊的方式推动玩家人数模型。这使我们能够建模，例如，持久和熟练的玩家对高难度更敏感，以及这样的玩家如何提早搅动，这使得玩家的数量以及难度与流失之间的关系随级别的水平而发展。我们的工作表明，即使是非常简单的人群级别的模拟个人玩家差异，也可以显着改善DRL游戏玩法的玩家行为预测，而无需为代理商付出昂贵的重新训练或为每个模拟玩家收集新的DRL游戏数据。
We propose a novel simulation model that is able to predict the per-level churn and pass rates of Angry Birds Dream Blast, a popular mobile free-to-play game. Our primary contribution is to combine AI gameplay using Deep Reinforcement Learning (DRL) with a simulation of how the player population evolves over the levels. The AI players predict level difficulty, which is used to drive a player population model with simulated skill, persistence, and boredom. This allows us to model, e.g., how less persistent and skilled players are more sensitive to high difficulty, and how such players churn early, which makes the player population and the relation between difficulty and churn evolve level by level. Our work demonstrates that player behavior predictions produced by DRL gameplay can be significantly improved by even a very simple population-level simulation of individual player differences, without requiring costly retraining of agents or collecting new DRL gameplay data for each simulated player.
