  This paper addresses the problem of online inverse reinforcement learning for
nonlinear systems with modeling uncertainties while in the presence of unknown
disturbances. The developed approach observes state and input trajectories for
an agent and identifies the unknown reward function online. Sub-optimality
introduced in the observed trajectories by the unknown external disturbance is
compensated for using a novel model-based inverse reinforcement learning
approach. The observer estimates the external disturbances and uses the
resulting estimates to learn the dynamic model of the demonstrator. The learned
demonstrator model along with the observed suboptimal trajectories are used to
implement inverse reinforcement learning. Theoretical guarantees are provided
using Lyapunov theory and a simulation example is shown to demonstrate the
effectiveness of the proposed technique.
