---
abstract: 'Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability. While hierarchy and partial observability are usually tackled separately (for instance by combining recurrent neural networks and options), we show that addressing both problems simultaneously is simpler and more efficient in many cases. More specifically, we make the initiation set of options conditional on the previously-executed option, and show that options with such Option-Observation Initiation Sets ([OOIs]{}) are at least as expressive as Finite State Controllers (FSCs), a state-of-the-art approach for learning in POMDPs. [OOIs]{}are easy to design based on an intuitive description of the task, lead to explainable policies and keep the top-level and option policies memoryless. Our experiments show that [OOIs]{}allow agents to learn optimal policies in challenging POMDPs, while being much more sample-efficient than a recurrent neural network over options.'
author:
- |
    Denis Steckelmacher\
    [**Diederik M. Roijers**]{}\
    [**Anna Harutyunyan**]{}\
    [**Peter Vrancx**]{}\
    [**Hélène Plisnier**]{}\
    [**Ann Nowé**]{}\
    Artificial Intelligence Lab, Vrije Universiteit Brussel, Belgium
bibliography:
- 'biblio.bib'
title: 'Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets'
---

Introduction
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Real-world applications of reinforcement learning (RL) face two main challenges: complex long-running tasks and partial observability. Options, 