Near-Optimal Representation Learning for Hierarchical Reinforcement
  Learning
  We study the problem of representation learning in goal-conditioned
hierarchical reinforcement learning. In such hierarchical structures, a
higher-level controller solves tasks by iteratively communicating goals which a
lower-level policy is trained to reach. Accordingly, the choice of
representation -- the mapping of observation space to goal space -- is crucial.
To study this problem, we develop a notion of sub-optimality of a
representation, defined in terms of expected reward of the optimal hierarchical
policy using this representation. We derive expressions which bound the
sub-optimality and show how these expressions can be translated to
representation learning objectives which may be optimized in practice. Results
on a number of difficult continuous-control tasks show that our approach to
representation learning yields qualitatively better representations as well as
quantitatively better hierarchical policies, compared to existing methods (see
videos at https://sites.google.com/view/representation-hrl).
