  Consistency is the theoretical property of a meta learning algorithm that
ensures that, under certain assumptions, it can adapt to any task at test time.
An open question is whether and how theoretical consistency translates into
practice, in comparison to inconsistent algorithms. In this paper, we
empirically investigate this question on a set of representative meta-RL
algorithms. We find that theoretically consistent algorithms can indeed usually
adapt to out-of-distribution (OOD) tasks, while inconsistent ones cannot,
although they can still fail in practice for reasons like poor exploration. We
further find that theoretically inconsistent algorithms can be made consistent
by continuing to update all agent components on the OOD tasks, and adapt as
well or better than originally consistent ones. We conclude that theoretical
consistency is indeed a desirable property, and inconsistent meta-RL algorithms
can easily be made consistent to enjoy the same benefits.
