  In this paper, a novel approach to the output-feedback inverse reinforcement
learning (IRL) problem is developed by casting the IRL problem, for linear
systems with quadratic cost functions, as a state estimation problem. Two
observer-based techniques for IRL are developed, including a novel observer
method that re-uses previous state estimates via history stacks. Theoretical
guarantees for convergence and robustness are established under appropriate
excitation conditions. Simulations demonstrate the performance of the developed
observers and filters under noisy and noise-free measurements.
