# Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2410.15837 · HTML: https://mlsanity.com/paper/2410.15837/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 8 of 8)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning](https://mlsanity.com/paper/2103.11686/) (2021)
- [Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shaping](https://mlsanity.com/paper/2209.14271/) (2022)
- [Neuro-Planner: A 3D Visual Navigation Method for MAV with Depth Camera based on Neuromorphic Reinforcement Learning](https://mlsanity.com/paper/2210.02305/) (2022)
- [Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning](https://mlsanity.com/paper/2403.05770/) (2024)
- [A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance](https://mlsanity.com/paper/2403.08808/) (2024)

## Built on by (most cited first, 2 of 2)

- [Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation](https://mlsanity.com/paper/2502.05069/) (2025)
- [Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learning](https://mlsanity.com/paper/2505.24572/) (2025)

## Abstract (arXiv)

Geomagnetic navigation has drawn increasing attention with its capacity in navigating through complex environments and its independence from external navigation services like global navigation satellite systems (GNSS). Existing studies on geomagnetic navigation, i.e., matching navigation and bionic navigation, rely on pre-stored map or extensive searches, leading to limited applicability or reduced navigation efficiency in unexplored areas. To address the issues with geomagnetic navigation in areas where GNSS is unavailable, this paper develops a deep reinforcement learning (DRL)-based mechanism, especially for long-distance geomagnetic navigation. The designed mechanism trains an agent to learn and gain the magnetoreception capacity for geomagnetic navigation, rather than using any pre-stored map or extensive and expensive searching approaches. Particularly, we integrate the geomagnetic gradient-based parallel approach into geomagnetic navigation. This integration mitigates the over-exploration of the learning agent by adjusting the geomagnetic gradient, such that the obtained gradient is aligned towards the destination. We explore the effectiveness of the proposed approach via detailed numerical simulations, where we implement twin delayed deep deterministic policy gradient (TD3) in realizing the proposed approach. The results demonstrate that our approach outperforms existing metaheuristic and bionic navigation methods in long-distance missions under diverse navigation conditions.