Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. In this article, we study the problem of training intelligent agents in service of game development. Unlike the agents built to “beat the game,” our agents aim to produce human-like behavior to help with game evaluation and balancing. We discuss two fundamental metrics based on which we measure the human-likeness of agents, namely skill and style, which are multifaceted concepts with practical implications outlined in this article. We report four case studies in which the style and skill requirements inform the choice of algorithms and metrics used to train agents; ranging from A* search to state-of-the-art deep reinforcement learning (RL). Furthermore, we, show that the learning potential of state-of-the-art deep RL models does not seamlessly transfer from the benchmark environments to target ones without heavily tuning their hyperparameters, leading to linear scaling of the engineering efforts, and computational cost with the number of target domains.

中文翻译：

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胜利不是一切：通过智能代理增强游戏开发

最近，智能体在学习与人类玩游戏并击败人类方面取得了几项引人注目的成就。在本文中，我们研究了在游戏开发服务中训练智能代理的问题。与为“击败游戏”而构建的代理不同，我们的代理旨在产生类人行为，以帮助进行游戏评估和平衡。我们讨论了衡量代理的人类相似性的两个基本指标，即技能和风格，这是本文概述的具有实际意义的多方面概念。我们报告了四个案例研究，其中风格和技能要求告知用于训练代理的算法和指标的选择；从 A* 搜索到最先进的深度强化学习 (RL)。此外，我们，