Shi, H.; Zhao, Z.; Chen, J.; Zhou, M.; Liu, Y. Enhancing UAV Path Planning in Multi-Agent Reinforcement Learning through Adaptive Dimensionality Reduction. Preprints2024, 2024082154. https://doi.org/10.20944/preprints202408.2154.v1
APA Style
Shi, H., Zhao, Z., Chen, J., Zhou, M., & Liu, Y. (2024). Enhancing UAV Path Planning in Multi-Agent Reinforcement Learning through Adaptive Dimensionality Reduction. Preprints. https://doi.org/10.20944/preprints202408.2154.v1
Chicago/Turabian Style
Shi, H., Mengjie Zhou and Yang Liu. 2024 "Enhancing UAV Path Planning in Multi-Agent Reinforcement Learning through Adaptive Dimensionality Reduction" Preprints. https://doi.org/10.20944/preprints202408.2154.v1
Abstract
Unmanned Aerial Vehicles (UAVs) have become increasingly important in various applications, including environmental monitoring, disaster response, and surveillance, due to their flexibility, efficiency, and ability to access hard-to-reach areas. Effective path planning for multiple UAVs exploring a target area is crucial for maximizing coverage and operational efficiency. This study presents a novel approach to optimizing collaborative navigation for UAVs using Multi-Agent Reinforcement Learning (MARL). To enhance the efficiency of this process, we introduce the Adaptive Dimensionality Reduction (ADR) framework, which includes Autoencoders (AEs) and Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. The ADR framework significantly reduces computational complexity by simplifying high-dimensional state spaces while preserving crucial information. Additionally, we incorporate communication modules to facilitate inter-UAV coordination, further improving path planning efficiency. Our experimental results demonstrate that the proposed approach significantly enhances exploration performance and reduces computational complexity, showcasing the potential of combining MARL with ADR techniques for advanced UAV navigation in complex environments.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
