In this study, we investigate the effects of conditioning Independent Q-Learners (IQL) not solely on the individual action-observation history, but additionally on the sufficient plan-time statistic for Decentralized Partially Observable Markov Decision Processes. In doing so, we attempt to address a key shortcoming of IQL, namely that it is likely to converge to a Nash Equilibrium that can be arbitrarily poor. We identify a novel exploration strategy for IQL when it conditions on the sufficient statistic, and furthermore show that sub-optimal equilibria can be escaped consistently by sequencing the decision-making during learning. The practical limitation is the exponential complexity of both the sufficient statistic and the decision rules.