  This paper develops a data-driven inverse reinforcement learning technique
for a class of linear systems to estimate the cost function of an agent online,
using input-output measurements. A simultaneous state and parameter estimator
is utilized to facilitate output-feedback inverse reinforcement learning, and
cost function estimation is achieved up to multiplication by a constant.
