  Reinforcement learning (RL) is a powerful framework for learning to take
actions to solve tasks. However, in many settings, an agent must winnow down
the inconceivably large space of all possible tasks to the single task that it
is currently being asked to solve. Can we instead constrain the space of tasks
to those that are semantically meaningful? In this work, we introduce a
framework for using weak supervision to automatically disentangle this
semantically meaningful subspace of tasks from the enormous space of
nonsensical "chaff" tasks. We show that this learned subspace enables efficient
exploration and provides a representation that captures distance between
states. On a variety of challenging, vision-based continuous control problems,
our approach leads to substantial performance gains, particularly as the
complexity of the environment grows.
