Unfortunately,optimizing anetworkusingthe desired evaluation metric, such asF1-Score, istypically not feasible.

In this work, we identify anovel set of conditions that ensure convergence with probability 1 ofQ-learning with linear function approximation, by proposing a twotime-scalevariationthereof.

In this paper, we initiate the study of Euclidean clustering with Distance-based privacy. Distance-based privacy is motivated by the fact that it is often only needed to protect the privacy of exact, rather than approximate, locations.