RobustImitationvia MirrorDescentInverseReinforcementLearning

Inspired by a first-order optimization method called mirror descent, this paper proposes topredict asequence ofrewardfunctions, which areiterativesolutions for a constrained convex problem. IRL solutions derived by mirror descent are tolerant totheuncertainty incurred bytargetdensity estimation sincetheamount of reward learning is regulated with respect to local geometric constraints.