  Learning the evolution of real-time strategy (RTS) game is a challenging
problem in artificial intelligent (AI) system. In this paper, we present a
novel Hebbian learning method to extract the global feature of point sets in
StarCraft II game units, and its application to predict the movement of the
points. Our model includes encoder, LSTM, and decoder, and we train the encoder
with the unsupervised learning method. We introduce the concept of neuron
activity aware learning combined with k-Winner-Takes-All. The optimal value of
neuron activity is mathematically derived, and experiments support the
effectiveness of the concept over the downstream task. Our Hebbian learning
rule benefits the prediction with lower loss compared to self-supervised
learning. Also, our model significantly saves the computational cost such as
activations and FLOPs compared to a frame-based approach.
