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We consider a setting in which the objective is to learn to navigate in acontrolled Markov process (CMP) where transition probabilities may abruptlychange. For this setting, we propose a performance measure called explorationsteps which counts the time steps at which the learner lacks sufficientknowledge to navigate its environment efficiently. We devise a learningmeta-algorithm, MNM and prove an upper bound on the exploration steps in termsof the number of changes.

Autonomous exploration for navigating in non-stationary CMPs