Enhancing Unmanned Aerial Vehicle Path Planning in Multi-Agent Reinforcement Learning through Adaptive Dimensionality Reduction

Enhancing Unmanned Aerial Vehicle Path Planning in Multi-Agent Reinforcement Learning through Adaptive Dimensionality Reduction

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...

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Bibliographic Details
Main Authors: Haotian Shi, Zilin Zhao, Jiale Chen, Mengjie Zhou, Yang Liu
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Drones
Subjects:
multi-agent reinforcement learning (MARL)
dimensionality reduction
unmanned aerial vehicles (UAVs)
Online Access:https://www.mdpi.com/2504-446X/8/10/521
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