  This paper proposes a data-driven, iterative approach for inverse optimal
control (IOC), which aims to learn the objective function of a nonlinear
optimal control system given its states and inputs. The approach solves the IOC
problem in a challenging situation when the system dynamics is unknown. The key
idea of the proposed approach comes from the deep Koopman representation of the
unknown system, which employs a deep neural network to represent observables
for the Koopman operator. By assuming the objective function to be learned is
parameterized as a linear combination of features with unknown weights, the
proposed approach for IOC is able to achieve a Koopman representation of the
unknown dynamics and the unknown weights in objective function together.
Simulation is provided to verify the proposed approach.
