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Reward machines have shown great promise at capturing non-Markovian rewardfunctions for learning tasks that involve complex action sequencing. However,no algorithm currently exists for learning reward machines with realistic weakfeedback in the form of preferences. We contribute REMAP, a novel algorithm forlearning reward machines from preferences, with correctness and terminationguarantees. REMAP introduces preference queries in place of membership queriesin the L* algorithm, and leverages a symbolic observation table along withunification and constraint solving to narrow the hypothesis reward machinesearch space. In addition to the proofs of correctness and termination forREMAP, we present empirical evidence measuring correctness: how frequently theresulting reward machine is isomorphic under a consistent yet inexact teacher,and the regret between the ground truth and learned reward machines.

Learning Reward Machines through Preference Queries over Sequences