Learning from small data sets is critical in
many practical applications where data col-
lection is time consuming or expensive, e.g.,
robotics, animal experiments or drug design.
Meta learning is one way to increase the data
efficiency of learning algorithms by general-
izing learned concepts from a set of training
tasks to unseen, but related, tasks. Often, this
relationship between tasks is hard coded or re-
lies in some other way on human expertise.
In this paper, we frame meta learning as a hi-
erarchical latent variable model and infer the
relationship between tasks automatically from
data. We apply our framework in a model-
based reinforcement learning setting and show
that our meta-learning model effectively gen-
eralizes to novel tasks by identifying how new
tasks relate to prior ones from minimal data.
This results in up to a
60%
reduction in the
average interaction time needed to solve tasks
compared to strong baselines.