This paper experimentally checks the hypothesis that capturing the predictive information is useful in RL. The novelty of the proposed auxiliary task lies in the fact that it learns a *compressed* representation of the predictive information. The experiments are convincing in showing the improvement of PI-SAC over SAC (with or without data augmentation) and over other approaches using auxiliary tasks.