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Inverse reinforcement learning (IRL) attempts to infer human rewards orpreferences from observed behavior. Since human planning systematicallydeviates from rationality, several approaches have been tried to account forspecific human shortcomings. However, the general problem of inferring thereward function of an agent of unknown rationality has received littleattention. Unlike the well-known ambiguity problems in IRL, this one ispractically relevant but cannot be resolved by observing the agent's policy inenough environments. This paper shows (1) that a No Free Lunch result impliesit is impossible to uniquely decompose a policy into a planning algorithm andreward function, and (2) that even with a reasonable simplicity prior/Occam'srazor on the set of decompositions, we cannot distinguish between the truedecomposition and others that lead to high regret. To address this, we needsimple `normative' assumptions, which cannot be deduced exclusively fromobservations.

Occam's razor is insufficient to infer the preferences of irrational agents