  The emerging field of \emph{value awareness engineering} claims that software
agents and systems should be value-aware, i.e. they must make decisions in
accordance with human values. In this context, such agents must be capable of
explicitly reasoning as to how far different courses of action are aligned with
these values. For this purpose, values are often modelled as preferences over
states or actions, which are then aggregated to determine the sequences of
actions that are maximally aligned with a certain value. Recently, additional
value admissibility constraints at this level have been considered as well.
  However, often relaxed versions of these constraints are needed, and this
increases considerably the complexity of computing value-aligned policies. To
obtain efficient algorithms that make value-aligned decisions considering
admissibility relaxation, we propose the use of learning techniques, in
particular, we have used constrained reinforcement learning algorithms. In this
paper, we present two algorithms, $\epsilon\text{-}ADQL$ for strategies based
on local alignment and its extension $\epsilon\text{-}CADQL$ for a sequence of
decisions. We have validated their efficiency in a water distribution problem
in a drought scenario.
