14 research outputs found
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for building
sample-efficient strategies that learn from past experience to adapt to any
task within a target class. Our goal is to equip the reader with the conceptual
foundations of this tool for building new, scalable agents that operate on
broad domains. To do so, we present basic algorithmic templates for building
near-optimal predictors and reinforcement learners which behave as if they had
a probabilistic model that allowed them to efficiently exploit task structure.
Furthermore, we recast memory-based meta-learning within a Bayesian framework,
showing that the meta-learned strategies are near-optimal because they amortize
Bayes-filtered data, where the adaptation is implemented in the memory dynamics
as a state-machine of sufficient statistics. Essentially, memory-based
meta-learning translates the hard problem of probabilistic sequential inference
into a regression problem.Comment: DeepMind Technical Report (15 pages, 6 figures