  Although deep reinforcement learning (deep RL) methods have lots of strengths
that are favorable if applied to autonomous driving, real deep RL applications
in autonomous driving have been slowed down by the modeling gap between the
source (training) domain and the target (deployment) domain. Unlike current
policy transfer approaches, which generally limit to the usage of
uninterpretable neural network representations as the transferred features, we
propose to transfer concrete kinematic quantities in autonomous driving. The
proposed robust-control-based (RC) generic transfer architecture, which we call
RL-RC, incorporates a transferable hierarchical RL trajectory planner and a
robust tracking controller based on disturbance observer (DOB). The deep RL
policies trained with known nominal dynamics model are transfered directly to
the target domain, DOB-based robust tracking control is applied to tackle the
modeling gap including the vehicle dynamics errors and the external
disturbances such as side forces. We provide simulations validating the
capability of the proposed method to achieve zero-shot transfer across multiple
driving scenarios such as lane keeping, lane changing and obstacle avoidance.
