论文标题
DeepCrawl：基于回合的策略游戏的深入加强学习
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games
论文作者
论文摘要
在本文中，我们介绍了DeepCrawl，这是一个完全可以播放的Roguelike原型和iOS和Android的原型，其中所有代理都由使用Deep Greenforcess学习（DRL）训练的策略网络控制。我们的目的是了解DRL的最新进展是否可用于开发视频游戏中非玩家角色的令人信服的行为模型。我们首先分析要求这样的AI系统应满足的要求，以便实际上适用于视频游戏开发，并确定Deeprawl原型中使用的DRL模型的元素。 DeepCrawl的成功和局限性通过在最后一场比赛中执行的一系列可玩性测试记录。我们认为，我们提出的技术为视频游戏中非玩家角色的行为发展创新的新途径提供了见解，因为它们提供了克服关键问题的潜力
In this paper we introduce DeepCrawl, a fully-playable Roguelike prototype for iOS and Android in which all agents are controlled by policy networks trained using Deep Reinforcement Learning (DRL). Our aim is to understand whether recent advances in DRL can be used to develop convincing behavioral models for non-player characters in videogames. We begin with an analysis of requirements that such an AI system should satisfy in order to be practically applicable in video game development, and identify the elements of the DRL model used in the DeepCrawl prototype. The successes and limitations of DeepCrawl are documented through a series of playability tests performed on the final game. We believe that the techniques we propose offer insight into innovative new avenues for the development of behaviors for non-player characters in video games, as they offer the potential to overcome critical issues with
