Moreover, weassumethatthedimensionsd and k are fixedthroughoutthispaper.

Van Seijen et al. [2017] propose to split a state into different sub-states, each with a sub-reward obtained bytraining ageneral valuefunction, andlearnmultiple valuefunctions withsub-rewards. The architecture is rather limited due to requiring prior knowledge of how to split into sub-states.

For additional motivation, it is reasonable to consider Massart noise to be a more realistic model of real-life noise (even when benign) when compared to the RCN model, as it allows for some amount of non-uniformity. This made Definition 1 a possibly tractable way to relax the noise assumption, without running intotheaforementioned computational barriers foragnostic learning.

Yet, small, distant, and incomplete objects with sparse or few points are often hard to detect.

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After observing a sample, the analyst may update their estimate of the mean and variance of that group and choose the next group accordingly. The analyst's objective is to dynamically collect samples to minimize the

Ontheotherhand,wefindthatstrong regularization is not necessary for learning high-quality feature representations.