  Is it possible to evaluate the moral cognition of complex artificial agents?
In this work, we take a look at one aspect of morality: `doing the right thing
for the right reasons.' We propose a behavior-based analysis of artificial
moral cognition which could also be applied to humans to facilitate
like-for-like comparison. Morally-motivated behavior should persist despite
mounting cost; by measuring an agent's sensitivity to this cost, we gain deeper
insight into underlying motivations. We apply this evaluation to a particular
set of deep reinforcement learning agents, trained by memory-based
meta-reinforcement learning. Our results indicate that agents trained with a
reward function that includes other-regarding preferences perform helping
behavior in a way that is less sensitive to increasing cost than agents trained
with more self-interested preferences.
