  This paper addresses the problem of online inverse reinforcement learning for
systems with limited data and uncertain dynamics. In the developed approach,
the state and control trajectories are recorded online by observing an agent
perform a task, and reward function estimation is performed in real-time using
a novel inverse reinforcement learning approach. Parameter estimation is
performed concurrently to help compensate for uncertainties in the agent's
dynamics. Data insufficiency is resolved by developing a data-driven update law
to estimate the optimal feedback controller. The estimated controller can then
be queried to artificially create additional data to drive reward function
estimation.
