# Constructing Abstraction Hierarchies Using a Skill-Symbol Loop

arXiv: https://arxiv.org/abs/1509.07582 · HTML: https://mlsanity.com/paper/1509.07582/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2015.

## Built on by (most cited first, 3 of 3)

- [An Analysis of Frame-skipping in Reinforcement Learning](https://mlsanity.com/paper/2102.03718/) (2021)
- [Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills](https://mlsanity.com/paper/2306.13630/) (2023)
- [A New View on Planning in Online Reinforcement Learning](https://mlsanity.com/paper/2406.01562/) (2024)

## Abstract (arXiv)

We describe a framework for building abstraction hierarchies whereby an agent alternates skill- and representation-acquisition phases to construct a sequence of increasingly abstract Markov decision processes. Our formulation builds on recent results showing that the appropriate abstract representation of a problem is specified by the agent's skills. We describe how such a hierarchy can be used for fast planning, and illustrate the construction of an appropriate hierarchy for the Taxi domain.