论文标题
通过Hebbian学习，在游戏代理商中预测群集的演变
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning
论文作者
论文摘要
大型多代理系统（例如实时策略游戏）通常是由代理的集体行为驱动的。例如，在《星际争霸二世》中，人类玩家在空间上靠近代理队进入一个团队，并控制球队以击败对手。从这个角度来看，将游戏中的代理聚类已用于各种目的，例如为游戏用户提供多代理增强学习和游戏分析工具中代理的有效控制。但是，尽管聚类提供了有用的信息，但很少研究在集群级别学习多代理系统的动态。在本文中，我们提出了一种混合AI模型，该模型将无监督和自我监督的学习融合在一起，以预测Starcraft II中群集的演变。我们在设定的群集模块中开发了一种无监督的HEBBIAN学习方法，以有效地创建比K-均值聚类的推理时间复杂性较低的群集的可变群集。同样，基于短期内存的长期预测模块旨在递归预测由设定到群集模块生成的状态向量，以定义群集配置。我们通过实验证明了所提出的模型成功地预测了游戏中簇的复杂运动。
Large multi-agent systems such as real-time strategy games are often driven by collective behavior of agents. For example, in StarCraft II, human players group spatially near agents into a team and control the team to defeat opponents. In this light, clustering the agents in the game has been used for various purposes such as the efficient control of the agents in multi-agent reinforcement learning and game analytic tools for the game users. However, despite the useful information provided by clustering, learning the dynamics of multi-agent systems at a cluster level has been rarely studied yet. In this paper, we present a hybrid AI model that couples unsupervised and self-supervised learning to forecast evolution of the clusters in StarCraft II. We develop an unsupervised Hebbian learning method in a set-to-cluster module to efficiently create a variable number of the clusters with lower inference time complexity than K-means clustering. Also, a long short-term memory based prediction module is designed to recursively forecast state vectors generated by the set-to-cluster module to define cluster configuration. We experimentally demonstrate the proposed model successfully predicts complex movement of the clusters in the game.
