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The leading last-iterate fluctuation in quantile temporal-difference learning can be controlled without polynomial dependence on the number of quantiles, challenging conventional assumptions about sample complexity.
Online inference in QTD can now be performed efficiently without the need to store entire trajectories, revolutionizing memory management in distributional reinforcement learning.
Achieving optimal sample efficiency in quantile-based distributional reinforcement learning could revolutionize how we evaluate policies in complex environments.
