126) suggests that options may already be close to addressing partial observability, thus removing the need for more complicated solutions. In this paper, we prove this intuition correct by:

1.  Showing that standard options do not suffice in POMDPs;

2.  Introducing Option-Observation Initiation Sets ([OOIs]{}), that make the initiation sets of options conditional on the previously-executed option;

3.  Proving that [OOIs]{}make options at least as expressive as Finite State Controllers (Section \[sec:fsc\]), thus able to tackle challenging POMDPs.

In contrast to existing HRL algorithms for POMDPs [@Wiering1997; @Theocharous2002; @Sridharan2010], [OOIs]{}handle repetitive tasks, do not restrict the action set available to sub-tasks, and keep the top-level and option policies memoryless. A wide range of robotic and simulated experiments in Section \[sec:experiments\] confirm that [OOIs]{}allow partially observable tasks to be solved optimally, demonstrate that [OOIs]{}are much more sample-efficient than a recurrent neural network over options, and illustrate the flexibility of [OOIs]{}regarding the amount of domain knowledge available at design time. In Section \[sec:treemaze\], we demonstrate the robustness of [OOIs]{}to sub-optimal option sets. While it is generally accepted that the designer provides the options and their initiation sets, we show in Section \[sec:duplicatedinput\] that random initiation sets, combined with learned option policies and 