Forecasting Evolution of Clusters in StarCraft II with Hebbian Learning
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Tactics in StarCraft II are closely related to group behavior of the game agents. In other words, human players in the game often group spatially near agents into a team and control the team to defeat opponents. In this light, clustering the agents in StarCraft II has been studied 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, these works do not aim to learn and predict dynamics of the clusters, limiting the applications to currently observed game status. 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, and it also features lower inference time complexity than conventional k-means clustering. For the prediction task, a long short-term memory based prediction module is designed to recursively forecast state vectors generated by the set-to-cluster module. We observe the proposed model successfully predicts complex evolution of the clusters with regard to cluster centroids and their radii.
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