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

Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving