Although there exist instance-dependent regret bounds for linear Markov decision processes (MDPs) and low-rank bandits, extensions to low-rank MDPs remain unexplored. In this work, we close this gap and provide regret bounds for low-rank MDPs in an instance-dependent setting. Specifically, we introduce an algorithm, called UNISREP-UCB, which utilizes a constrained optimization objective to learn features with good spectral properties. Furthermore, we demonstrate that our algorithm enjoys constant regret if the minimal sub-optimality gap and the occupancy distribution of the optimal policy are well-defined and known. To the best of our knowledge, these are the first instance-dependent regret results for low-rank MDPs.

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