# Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation

arXiv: https://arxiv.org/abs/1807.05424 · HTML: https://mlsanity.com/paper/1807.05424/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2018.

## Built on by (most cited first, 7 of 7)

- [Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration](https://mlsanity.com/paper/2301.03398/) (2023)
- [Conflict Avoidance in Social Navigation -- a Survey](https://mlsanity.com/paper/2106.12113/) (2021)
- [Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk](https://mlsanity.com/paper/2212.04123/) (2022)
- [Unsupervised Neural Hidden Markov Models with a Continuous latent state space](https://mlsanity.com/paper/2106.06536/) (2021)
- [Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance](https://mlsanity.com/paper/2201.02397/) (2022)
- [GUIDE: Goal-Initialized Directional Understanding for End-to-End Legged Navigation](https://mlsanity.com/paper/2606.10832/) (2026)
- [Emergence of Agentic AI: A Review on Evolution, Background, Working Principles, Applications, Adoption Factors, and Future Research Directions](https://mlsanity.com/paper/2608.18110/) (2026)

## Abstract (arXiv)

In this paper, we propose a navigation algorithm oriented to multi-agent environment. This algorithm is expressed as a hierarchical framework that contains a Hidden Markov Model (HMM) and a Deep Reinforcement Learning (DRL) structure. For simplification, we term our method Hierarchical Navigation Reinforcement Network (HNRN). In high- level architecture, we train an HMM to evaluate the agent's perception to obtain a score. According to this score, adaptive control action will be chosen. While in low-level architecture, two sub-systems are introduced, one is a differential target- driven system, which aims at heading to the target; the other is a collision avoidance DRL system, which is used for avoiding dynamic obstacles. The advantage of this hierarchical structure is decoupling the target-driven and collision avoidance tasks, leading to a faster and more stable model to be trained. The experiments indicate that our algorithm has higher learning efficiency and rate of success than traditional Velocity Obstacle (VO) algorithms or hybrid DRL method.