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Meta-trained agents implement Bayes-optimal agents
Vladimir Mikulik · Grégoire Delétang · Tom McGrath · Tim Genewein · Miljan Martic · Shane Legg · Pedro Ortega

Wed Dec 09 07:00 AM -- 07:10 AM (PST) @ Orals & Spotlights: Continual/Meta/Misc Learning
in Orals & Spotlights Track 16: Continual/Meta/Misc Learning »

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 is a general technique for numerically approximating Bayes-optimal agents; that is, even for task distributions for which we currently don't possess tractable models.

[ Paper ]

Author Information

Vladimir Mikulik (Google DeepMind)
Grégoire Delétang (DeepMind)
Tom McGrath (Deepmind)
Tim Genewein (DeepMind)
Miljan Martic (DeepMind)
Shane Legg (DeepMind)
Pedro Ortega (DeepMind)

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