According to previous studies, one of the major impediments to accurate off-policy learning is the overestimation bias. This paper investigates a novel way to alleviate the overestimation bias in a continuous control setting. Our method—Truncated Quantile Critics, TQC,—blends three ideas: distributional representation of a critic, truncation of critics prediction, and ensembling of multiple critics. We show that all components are key for the achieved performance. Distributional representation c…