  Vector Quantization (VQ) is a method for discretizing latent representations
and has become a major part of the deep learning toolkit. It has been
theoretically and empirically shown that discretization of representations
leads to improved generalization, including in reinforcement learning where
discretization can be used to bottleneck multi-agent communication to promote
agent specialization and robustness. The discretization tightness of most
VQ-based methods is defined by the number of discrete codes in the
representation vector and the codebook size, which are fixed as
hyperparameters. In this work, we propose learning to dynamically select
discretization tightness conditioned on inputs, based on the hypothesis that
data naturally contains variations in complexity that call for different levels
of representational coarseness. We show that dynamically varying tightness in
communication bottlenecks can improve model performance on visual reasoning and
reinforcement learning tasks.
