Human explanation (e.g.,interms offeature importance) hasbeenrecently used to extend the communication channel between human and agent in interactive machinelearning.

A version ofthis bound becomes tight under certain assumptions.

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.