  A characteristic of reinforcement learning is the ability to develop
unforeseen strategies when solving problems. While such strategies sometimes
yield superior performance, they may also result in undesired or even dangerous
behavior. In industrial scenarios, a system's behavior also needs to be
predictable and lie within defined ranges. To enable the agents to learn (how)
to align with a given specification, this paper proposes to explicitly transfer
functional and non-functional requirements into shaped rewards. Experiments are
carried out on the smart factory, a multi-agent environment modeling an
industrial lot-size-one production facility, with up to eight agents and
different multi-agent reinforcement learning algorithms. Results indicate that
compliance with functional and non-functional constraints can be achieved by
the proposed approach.
