# Hierarchical Reinforcement Learning in Multi-Goal Spatial Navigation with Autonomous Mobile Robots

arXiv: https://arxiv.org/abs/2504.18794 · HTML: https://mlsanity.com/paper/2504.18794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 26 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 17 of 17)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Data-Efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1805.08296/) (2018)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [FeUdal Networks for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1703.01161/) (2017)
- [Maximum a Posteriori Policy Optimisation](https://mlsanity.com/paper/1806.06920/) (2018)
- [Reverse Curriculum Generation for Reinforcement Learning](https://mlsanity.com/paper/1707.05300/) (2017)
- [Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?](https://mlsanity.com/paper/1909.10618/) (2019)
- [Hindsight policy gradients](https://mlsanity.com/paper/1711.06006/) (2017)
- [Options of Interest: Temporal Abstraction with Interest Functions](https://mlsanity.com/paper/2001.00271/) (2020)
- [Learnings Options End-to-End for Continuous Action Tasks](https://mlsanity.com/paper/1712.00004/) (2017)
- [The Termination Critic](https://mlsanity.com/paper/1902.09996/) (2019)
- [Attention Option-Critic](https://mlsanity.com/paper/2201.02628/) (2022)

## Built on by (most cited first, 1 of 1)

- [Enter the Void - Planning to Seek Entropy When Reward is Scarce](https://mlsanity.com/paper/2505.16787/) (2025)

## Abstract (arXiv)

Hierarchical reinforcement learning (HRL) is hypothesized to be able to leverage the inherent hierarchy in learning tasks where traditional reinforcement learning (RL) often fails. In this research, HRL is evaluated and contrasted with traditional RL in complex robotic navigation tasks. We evaluate unique characteristics of HRL, including its ability to create sub-goals and the termination functions. We constructed a number of experiments to test: 1) the differences between RL proximal policy optimization (PPO) and HRL, 2) different ways of creating sub-goals in HRL, 3) manual vs automatic sub-goal creation in HRL, and 4) the effects of the frequency of termination on performance in HRL. These experiments highlight the advantages of HRL over RL and how it achieves these advantages.