BRL: Learning behavior representations of Reversi players

Player behavior modeling is of the utmost importance in game development and player matching. This problem is challenging because behavior is multi-semantic and hard to represent. Existing work often suffers from low generalization ability and high demand for supervisory information. In this paper, we present a behavior representation learning method (BRL) for Reversi players. It learns entirely from unlabeled game records. First, we develop an asymmetric encoder-decoder architecture to learn the mapping between states and actions. The encoder maps game records into a latent subspace for behavior representation. Second, we mask random states to force the encoder to capture the high-level features of the policy. The decoder predicts the corresponding actions according to the latent representation. Coupling these two designs, the semantic relevance of behaviors can be more effectively measured. Experiments were conducted in Reversi, using 14,000 game records of different players to learn behavior representations. Transfer performance in downstream tasks outperforms the supervised method and shows promising scaling ability. This work opens a new way for analyzing and modeling player behavior.