In industrial applications of adaptive optimal control often multiple
contrary objectives have to be considered. The weights (relative importance) of
the objectives are often not known during the design of the control and can
change with changing production conditions and requirements. In this work a
novel model-free multiobjective reinforcement learning approach for adaptive
optimal control of manufacturing processes is proposed. The approach enables
sample-efficient learning in sequences of control configurations, given by
particular objective weights.Comment: Conference, Preprint, 978-1-5386-5925-0/18/$31.00 \c{opyright} 2018
IEE