  We consider a Bayesian approach to offline model-based inverse reinforcement
learning (IRL). The proposed framework differs from existing offline
model-based IRL approaches by performing simultaneous estimation of the
expert's reward function and subjective model of environment dynamics. We make
use of a class of prior distributions which parameterizes how accurate the
expert's model of the environment is to develop efficient algorithms to
estimate the expert's reward and subjective dynamics in high-dimensional
settings. Our analysis reveals a novel insight that the estimated policy
exhibits robust performance when the expert is believed (a priori) to have a
highly accurate model of the environment. We verify this observation in the
MuJoCo environments and show that our algorithms outperform state-of-the-art
offline IRL algorithms.
