  Model Predictive Control (MPC) is attracting tremendous attention in the
autonomous driving task as a powerful control technique. The success of an MPC
controller strongly depends on an accurate internal dynamics model. However,
the static parameters, usually learned by system identification, often fail to
adapt to both internal and external perturbations in real-world scenarios. In
this paper, we firstly (1) reformulate the problem as a Partially Observed
Markov Decision Process (POMDP) that absorbs the uncertainties into
observations and maintains Markov property into hidden states; and (2) learn a
recurrent policy continually adapting the parameters of the dynamics model via
Recurrent Reinforcement Learning (RRL) for optimal and adaptive control; and
(3) finally evaluate the proposed algorithm (referred as $\textit{MPC-RRL}$) in
CARLA simulator and leading to robust behaviours under a wide range of
perturbations.
