During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents perform asignificant number ofrandom exploratory steps.

However, this lower bound only holds against deterministic preconditioners, and in many contexts randomization is crucial to the success of preconditioners. We prove a stronger lower bound that rules out randomized preconditioners.

Its central concept is the fractal dimension that measures the distributive irregularity of a spatial point set.

Somelaterapproaches [40, 28, 23, 22, 38, 43] try to explicitly model this relationship in a learnable fashion.

Detecting and correcting bias is challenging, as ratings are subjective and unverifiable. Unlike previous works relying on prior knowledge or historical data, we propose a one-shot noise calibration process without any prior information.