  We study the inverse reinforcement learning (IRL) problem under a transition
dynamics mismatch between the expert and the learner. Specifically, we consider
the Maximum Causal Entropy (MCE) IRL learner model and provide a tight upper
bound on the learner's performance degradation based on the $\ell_1$-distance
between the transition dynamics of the expert and the learner. Leveraging
insights from the Robust RL literature, we propose a robust MCE IRL algorithm,
which is a principled approach to help with this mismatch. Finally, we
empirically demonstrate the stable performance of our algorithm compared to the
standard MCE IRL algorithm under transition dynamics mismatches in both finite
and continuous MDP problems.
