  On playing video games, different players usually have their own playstyles.
Recently, there have been great improvements for the video game AIs on the
playing strength. However, past researches for analyzing the behaviors of
players still used heuristic rules or the behavior features with the
game-environment support, thus being exhausted for the developers to define the
features of discriminating various playstyles. In this paper, we propose the
first metric for video game playstyles directly from the game observations and
actions, without any prior specification on the playstyle in the target game.
Our proposed method is built upon a novel scheme of learning discrete
representations that can map game observations into latent discrete states,
such that playstyles can be exhibited from these discrete states. Namely, we
measure the playstyle distance based on game observations aligned to the same
states. We demonstrate high playstyle accuracy of our metric in experiments on
some video game platforms, including TORCS, RGSK, and seven Atari games, and
for different agents including rule-based AI bots, learning-based AI bots, and
human players.
