  Inverse reinforcement learning (IRL) attempts to infer human rewards or
preferences from observed behavior. Since human planning systematically
deviates from rationality, several approaches have been tried to account for
specific human shortcomings. However, the general problem of inferring the
reward function of an agent of unknown rationality has received little
attention. Unlike the well-known ambiguity problems in IRL, this one is
practically relevant but cannot be resolved by observing the agent's policy in
enough environments. This paper shows (1) that a No Free Lunch result implies
it is impossible to uniquely decompose a policy into a planning algorithm and
reward function, and (2) that even with a reasonable simplicity prior/Occam's
razor on the set of decompositions, we cannot distinguish between the true
decomposition and others that lead to high regret. To address this, we need
simple `normative' assumptions, which cannot be deduced exclusively from
observations.
