  The numerous deployed Artificial Intelligence systems need to be aligned with
our ethical considerations. However, such ethical considerations might change
as time passes: our society is not fixed, and our social mores evolve. This
makes it difficult for these AI systems; in the Machine Ethics field
especially, it has remained an under-studied challenge. In this paper, we
present two algorithms, named QSOM and QDSOM, which are able to adapt to
changes in the environment, and especially in the reward function, which
represents the ethical considerations that we want these systems to be aligned
with. They associate the well-known Q-Table to (Dynamic) Self-Organizing Maps
to handle the continuous and multi-dimensional state and action spaces. We
evaluate them on a use-case of multi-agent energy repartition within a small
Smart Grid neighborhood, and prove their ability to adapt, and their higher
performance compared to baseline Reinforcement Learning algorithms.
