  Memory-based meta-learning is a powerful technique to build agents that adapt
fast to any task within a target distribution. A previous theoretical study has
argued that this remarkable performance is because the meta-training protocol
incentivises agents to behave Bayes-optimally. We empirically investigate this
claim on a number of prediction and bandit tasks. Inspired by ideas from
theoretical computer science, we show that meta-learned and Bayes-optimal
agents not only behave alike, but they even share a similar computational
structure, in the sense that one agent system can approximately simulate the
other. Furthermore, we show that Bayes-optimal agents are fixed points of the
meta-learning dynamics. Our results suggest that memory-based meta-learning
might serve as a general technique for numerically approximating Bayes-optimal
agents - that is, even for task distributions for which we currently don't
possess tractable models.
