Learning Transferable Concepts in Deep Reinforcement Learning
  While humans and animals learn incrementally during their lifetimes and
exploit their experience to solve new tasks, standard deep reinforcement
learning methods specialize to solve only one task at a time and, as a result,
the information they acquire is hardly reusable in new situations. Here, we
introduce a new perspective on the problem of leveraging prior knowledge to
solve future unknown tasks. We show that learning discrete concept-like
representations of sensory inputs can provide a high-level abstraction that is
common across multiple tasks, thus facilitating the transference of
information. In particular, we show that it is possible to learn such
representations by self-supervision, following an information theoretic
approach, and that they improve the sample efficiency by providing prior
policies that guide the policy learning process. Our method is able to learn
concepts in locomotive tasks that reduce the number of optimization steps in
both known and unknown tasks, opening a new path to endow artificial agents
with generalization abilities.
