Mark introduces a distributional approach to reinforcement learning that offers significant performance advantages by backing up distributions instead of single values. Despite the growing body of research, the underlying reasons for these benefits remain unclear. Claire's recent work challenges the traditional expectational methods, revealing that there may be no fundamental difference in outcomes between the two approaches.
