  This paper focuses on the development of an online inverse reinforcement
learning (IRL) technique for a class of nonlinear systems. The developed
approach utilizes observed state and input trajectories, and determines the
unknown cost function and the unknown value function online. A parameter
estimation technique is utilized to allow the developed IRL technique to
determine the cost function weights in the presence of unknown dynamics.
Simulation results are presented for a nonlinear system showing convergence of
both unknown reward function weights and unknown dynamics.
