  We introduce Hindsight Off-policy Options (HO2), a data-efficient option
learning algorithm. Given any trajectory, HO2 infers likely option choices and
backpropagates through the dynamic programming inference procedure to robustly
train all policy components off-policy and end-to-end. The approach outperforms
existing option learning methods on common benchmarks. To better understand the
option framework and disentangle benefits from both temporal and action
abstraction, we evaluate ablations with flat policies and mixture policies with
comparable optimization. The results highlight the importance of both types of
abstraction as well as off-policy training and trust-region constraints,
particularly in challenging, simulated 3D robot manipulation tasks from raw
pixel inputs. Finally, we intuitively adapt the inference step to investigate
the effect of increased temporal abstraction on training with pre-trained
options and from scratch.
