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On the Practical Consistency of Meta-Reinforcement Learning Algorithms
[article]
2021
arXiv
pre-print
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,
arXiv:2112.00478v1
fatcat:esoasqbwajddzkqhhum536ijeu