mlsanityData to 14 Oct 2026

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

arXiv 2504.18794 ↗First posted 26 Apr 2025cs.AI cs.ROcited by 1

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

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.

Key facts. Cited by 1 paper 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.

Builds on

17 of 42 references.

References · 17

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5 more
  1. 2017Hindsight policy gradientscited by 37
  2. 2020Options of Interest: Temporal Abstraction with Interest Functionscited by 30
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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Enter the Void - Planning to Seek Entropy When Reward is Scarcecited by 0

Similar, not cited

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  1. 2022Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning
  2. 2024Reconciling Spatial and Temporal Abstractions for Goal Representation
  3. 2023Goal Space Abstraction in Hierarchical Reinforcement Learning via Reachability Analysis
  4. 2025Hierarchical Reinforcement Learning for Safe Mapless Navigation with Congestion Estimation
  5. 2024Exploring the limits of Hierarchical World Models in Reinforcement Learning
  6. 2024On the benefits of pixel-based hierarchical policies for task generalization
  7. 2025Proposing Hierarchical Goal-Conditioned Policy Planning in Multi-Goal Reinforcement Learning
  8. 2026On Semi-Markov Suboptimality in Hierarchical Reinforcement Learning