In interactive MORL, the user is regularly queried during the algorithm learning phase so that their preferences can be learned.
(figure from A practical guide to multi-objective reinforcement learning and planning)
The general method is to start with a prior over weights, and regularly query the user during learning (usually presenting them with pairwise comparisons). The results of these queries is used to update the posterior, usually in a Bayesian manner.