  In this work, we develop an automated method to generate 3D human walking
motion in simulation which is comparable to real-world human motion. At the
core, our work leverages the ability of deep reinforcement learning methods to
learn high-dimensional motor skills while being robust to variations in the
environment dynamics. Our approach iterates between policy learning and
parameter identification to match the real-world bio-mechanical human data. We
present a thorough evaluation of the kinematics, kinetics and ground reaction
forces generated by our learned virtual human agent. We also show that the
method generalizes well across human-subjects with different kinematic
structure and gait-characteristics.
