  We study inverse reinforcement learning (IRL) and imitation learning (IM),
the problems of recovering a reward or policy function from expert's
demonstrated trajectories. We propose a new way to improve the learning process
by adding a weight function to the maximum entropy framework, with the
motivation of having the ability to learn and recover the stochasticity (or the
bounded rationality) of the expert policy. Our framework and algorithms allow
to learn both a reward (or policy) function and the structure of the entropy
terms added to the Markov Decision Processes, thus enhancing the learning
procedure. Our numerical experiments using human and simulated demonstrations
and with discrete and continuous IRL/IM tasks show that our approach
outperforms prior algorithms.
