Unsupervised Hebbian Learning on Point Sets in StarCraft II
This work addresses a specific challenge in AI for real-time strategy games, offering incremental improvements in efficiency and performance for game unit prediction tasks.
The paper tackles the problem of predicting unit movements in StarCraft II by proposing an unsupervised Hebbian learning method for point sets, resulting in lower prediction loss and reduced computational costs compared to self-supervised and frame-based approaches.
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.