The highest grossing media franchise of all times, with over \$90 billion in total revenue, is Pokemon. The video games belong to the class of Japanese Role Playing Games (J-RPG). Developing a powerful AI agent for these games is very hard because they present big challenges to MinMax, Monte Carlo Tree Search and statistical Machine Learning, as they are vastly different from the well explored in AI literature games. An AI agent for one of these games means significant progress in AI agents for the entire class. Further, the key principles of such work can hopefully inspire approaches to several domains that require excellent teamwork under conditions of extreme uncertainty, including managing a team of doctors, robots or employees in an ever changing environment, like a pandemic stricken region or a war-zone. In this paper we first explain the mechanics of the game and we perform a game analysis. We continue by proposing unique AI algorithms based on our understanding that the two biggest challenges in the game are keeping a balanced team and dealing with three sources of uncertainty. Later on, we describe why evaluating the performance of such agents is challenging and we present the results of our approach. Our AI agent performed significantly better than all previous attempts and peaked at the 33rd place in the world, in one of the most popular battle formats, while running on only 4 single socket servers.
翻译:有史以来最大的媒体总总专营权是Pokemon。视频游戏属于日本角色游戏(J-RPG)的类别。为这些游戏开发一个强大的AI代理机构非常困难,因为它们对Minmax、蒙特卡洛树搜索和统计机学习提出了巨大的挑战,因为它们与AI文献游戏中探索的很好的情况大不相同。其中一个游戏的AI代理机构意味着全阶层的AI代理机构取得重大进展。此外,这种工作的关键原则有望激励在极端不确定的条件下,对需要出色团队合作的几个领域采取各种办法,包括管理一个医生、机器人或雇员团队,在不断变化的环境中,如大流行病肆虐地区或战区。在这个文件中,我们首先解释游戏的机理,我们进行游戏分析。我们继续提出独特的AI算法,我们的理解是游戏中两个最大的挑战是保持一个平衡的团队,并处理三个不确定因素。我们接下来要说明为什么评估这些代理机构的业绩是富有挑战性的,我们介绍我们的方法的结果。我们的AI代理机构在前一个最高级的服务器中,在前一个最高级的服务器上进行了比前一个最高级的三重的三重。