# Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization

arXiv: https://arxiv.org/abs/2004.04618 · HTML: https://mlsanity.com/paper/2004.04618/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023)
- [Deep Transfer Learning for WiFi Localization](https://mlsanity.com/paper/2103.05123/) (2021)
- [DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection](https://mlsanity.com/paper/2108.13157/) (2021)
- [STMA: A Spatio-Temporal Memory Agent for Long-Horizon Embodied Task Planning](https://mlsanity.com/paper/2502.10177/) (2025)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.05248/) (2022)
- [JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization](https://mlsanity.com/paper/2205.08422/) (2022)
- [DCoPilot: Generative AI-Empowered Policy Adaptation for Dynamic Data Center Operations](https://mlsanity.com/paper/2602.02137/) (2026)
- [Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion](https://mlsanity.com/paper/2210.15132/) (2022)

## Abstract (arXiv)

Location is key to spatialize internet-of-things (IoT) data. However, it is challenging to use low-cost IoT devices for robust unsupervised localization (i.e., localization without training data that have known location labels). Thus, this paper proposes a deep reinforcement learning (DRL) based unsupervised wireless-localization method. The main contributions are as follows. (1) This paper proposes an approach to model a continuous wireless-localization process as a Markov decision process (MDP) and process it within a DRL framework. (2) To alleviate the challenge of obtaining rewards when using unlabeled data (e.g., daily-life crowdsourced data), this paper presents a reward-setting mechanism, which extracts robust landmark data from unlabeled wireless received signal strengths (RSS). (3) To ease requirements for model re-training when using DRL for localization, this paper uses RSS measurements together with agent location to construct DRL inputs. The proposed method was tested by using field testing data from multiple Bluetooth 5 smart ear tags in a pasture. Meanwhile, the experimental verification process reflected the advantages and challenges for using DRL in wireless localization.