  This work presents a multiscale framework to solve an inverse reinforcement
learning (IRL) problem for continuous-time/state stochastic systems. We take
advantage of a diffusion wavelet representation of the associated Markov chain
to abstract the state space. This not only allows for effectively handling the
large (and geometrically complex) decision space but also provides more
interpretable representations of the demonstrated state trajectories and also
of the resulting policy of IRL. In the proposed framework, the problem is
divided into the global and local IRL, where the global approximation of the
optimal value functions are obtained using coarse features and the local
details are quantified using fine local features. An illustrative numerical
example on robot path control in a complex environment is presented to verify
the proposed method.
