My understanding of tabular Q-learning is that it essentially builds a dictionary of state-action pairs, so as to maximize the Markovian (i.e., step-wise, history-agnostic?) reward. This incremental update of the Q-table can be done by a trade-off exploration and exploitation, but the fact remains that one "walks around" the table until it converges to optimality.
But what if we haven't "walked around" the whole table? Can the algorithm still perform well in those out-of-sample state-action pairs?