# Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation

arXiv: https://arxiv.org/abs/2502.05069 · HTML: https://mlsanity.com/paper/2502.05069/

## 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 12 of its 49 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## 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, 12 of 12)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research](https://mlsanity.com/paper/1802.09464/) (2018)
- [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)
- [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)
- [GAN-supervised Seismic Data Reconstruction: An Enhanced-Learning for Improved Generalization](https://mlsanity.com/paper/2311.10910/) (2023)
- [Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning](https://mlsanity.com/paper/2410.15837/) (2024)

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

- [Ultra-short-term solar power forecasting by deep learning and data reconstruction](https://mlsanity.com/paper/2509.17095/) (2025)
- [Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learning](https://mlsanity.com/paper/2505.24572/) (2025)

## Abstract (arXiv)

The advancement in autonomous vehicles has empowered navigation and exploration in unknown environments. Geomagnetic navigation for autonomous vehicles has drawn increasing attention with its independence from GPS or inertial navigation devices. While geomagnetic navigation approaches have been extensively investigated, the generalizability of learned geomagnetic navigation strategies remains unexplored. The performance of a learned strategy can degrade outside of its source domain where the strategy is learned, due to a lack of knowledge about the geomagnetic characteristics in newly entered areas. This paper explores the generalization of learned geomagnetic navigation strategies via deep reinforcement learning (DRL). Particularly, we employ DRL agents to learn multiple teacher models from distributed domains that represent dispersed navigation strategies, and amalgamate the teacher models for generalizability across navigation areas. We design a reward shaping mechanism in training teacher models where we integrate both potential-based and intrinsic-motivated rewards. The designed reward shaping can enhance the exploration efficiency of the DRL agent and improve the representation of the teacher models. Upon the gained teacher models, we employ multi-teacher policy distillation to merge the policies learned by individual teachers, leading to a navigation strategy with generalizability across navigation domains. We conduct numerical simulations, and the results demonstrate an effective transfer of the learned DRL model from a source domain to new navigation areas. Compared to existing evolutionary-based geomagnetic navigation methods, our approach provides superior performance in terms of navigation length, duration, heading deviation, and success rate in cross-domain navigation.