  In this paper, we present a Deep Reinforcement Learning (RL)-driven Adaptive
Stochastic Nonlinear Model Predictive Control (SNMPC) to optimize uncertainty
handling, constraints robustification, feasibility, and closed-loop
performance. To this end, we conceive an RL agent to proactively anticipate
upcoming control tasks and to dynamically determine the most suitable
combination of key SNMPC parameters - foremost the robustification factor
$\kappa$ and the Uncertainty Propagation Horizon (UPH) $T_u$. We analyze the
trained RL agent's decision-making process and highlight its ability to learn
context-dependent optimal parameters. One key finding is that adapting the
constraints robustification factor with the learned policy reduces conservatism
and improves closed-loop performance while adapting UPH renders previously
infeasible SNMPC problems feasible when faced with severe disturbances. We
showcase the enhanced robustness and feasibility of our Adaptive SNMPC (aSNMPC)
through the real-time motion control task of an autonomous passenger vehicle to
follow an optimal race line when confronted with significant time-variant
disturbances. Experimental findings demonstrate that our look-ahead RL-driven
aSNMPC outperforms its Static SNMPC (sSNMPC) counterpart in minimizing the
lateral deviation both with accurate and inaccurate disturbance assumptions and
even when driving in previously unexplored environments.
