  Determining the optimal cost function parameters of Model Predictive Control
(MPC) to optimize multiple control objectives is a challenging and
time-consuming task. Multiobjective Bayesian Optimization (BO) techniques solve
this problem by determining a Pareto optimal parameter set for an MPC with
static weights. However, a single parameter set may not deliver the most
optimal closed-loop control performance when the context of the MPC operating
conditions changes during its operation, urging the need to adapt the cost
function weights at runtime. Deep Reinforcement Learning (RL) algorithms can
automatically learn context-dependent optimal parameter sets and dynamically
adapt for a Weightsvarying MPC (WMPC). However, learning cost function weights
from scratch in a continuous action space may lead to unsafe operating states.
To solve this, we propose a novel approach limiting the RL actions within a
safe learning space representing a catalog of pre-optimized BO Pareto-optimal
weight sets. We conceive a RL agent not to learn in a continuous space but to
proactively anticipate upcoming control tasks and to choose the most optimal
discrete actions, each corresponding to a single set of Pareto optimal weights,
context-dependent. Hence, even an untrained RL agent guarantees a safe and
optimal performance. Experimental results demonstrate that an untrained RL-WMPC
shows Pareto-optimal closed-loop behavior and training the RL-WMPC helps
exhibit a performance beyond the Pareto-front.
