  Augmenting an agent's control with useful higher-level behaviors called
options can greatly reduce the sample complexity of reinforcement learning, but
manually designing options is infeasible in high-dimensional and abstract state
spaces. While recent work has proposed several techniques for automated option
discovery, they do not scale to multi-level hierarchies and to expressive
representations such as deep networks. We present Discovery of Deep Options
(DDO), a policy-gradient algorithm that discovers parametrized options from a
set of demonstration trajectories, and can be used recursively to discover
additional levels of the hierarchy. The scalability of our approach to
multi-level hierarchies stems from the decoupling of low-level option discovery
from high-level meta-control policy learning, facilitated by
under-parametrization of the high level. We demonstrate that using the
discovered options to augment the action space of Deep Q-Network agents can
accelerate learning by guiding exploration in tasks where random actions are
unlikely to reach valuable states. We show that DDO is effective in adding
options that accelerate learning in 4 out of 5 Atari RAM environments chosen in
our experiments. We also show that DDO can discover structure in robot-assisted
surgical videos and kinematics that match expert annotation with 72% accuracy.
