Title: The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI,
Abstract: We present a vision-only model for gaming AI which uses a late integration
deep convolutional network architecture trained in a purely supervised
imitation learning context. Although state-of-the-art deep learning models for
video game tasks generally rely on more complex methods such as deep-Q
learning, we show that a supervised model which requires substantially fewer
resources and training time can already perform well at human reaction speeds
on the N64 classic game Super Smash Bros. We frame our learning task as a
30-class classification problem, and our CNN model achieves 80% top-1 and 95%
top-3 validation accuracy. With slight test-time fine-tuning, our model is also
competitive during live simulation with the highest-level AI built into the
game. We will further show evidence through network visualizations that the
network is successfully leveraging temporal information during inference to aid
in decision making. Our work demonstrates that supervised CNN models can
provide good performance in challenging policy prediction tasks while being
significantly simpler and more lightweight than alternatives.