# Hierarchical Reinforcement Learning with Abductive Planning

arXiv: https://arxiv.org/abs/1806.10792 · HTML: https://mlsanity.com/paper/1806.10792/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Intelligent problem-solving as integrated hierarchical reinforcement learning](https://mlsanity.com/paper/2208.08731/) (2022)
- [Hierarchies of Planning and Reinforcement Learning for Robot Navigation](https://mlsanity.com/paper/2109.11178/) (2021)
- [Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation](https://mlsanity.com/paper/2511.02303/) (2025)
- [Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming](https://mlsanity.com/paper/2106.11417/) (2021)
- [Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs](https://mlsanity.com/paper/2307.12063/) (2023)

## Abstract (arXiv)

One of the key challenges in applying reinforcement learning to real-life problems is that the amount of train-and-error required to learn a good policy increases drastically as the task becomes complex. One potential solution to this problem is to combine reinforcement learning with automated symbol planning and utilize prior knowledge on the domain. However, existing methods have limitations in their applicability and expressiveness. In this paper we propose a hierarchical reinforcement learning method based on abductive symbolic planning. The planner can deal with user-defined evaluation functions and is not based on the Herbrand theorem. Therefore it can utilize prior knowledge of the rewards and can work in a domain where the state space is unknown. We demonstrate empirically that our architecture significantly improves learning efficiency with respect to the amount of training examples on the evaluation domain, in which the state space is unknown and there exist multiple goals.