DTPPO: Dual-Transformer Encoder-Based Proximal Policy Optimization for Multi-UAV Navigation in Unseen Complex Environments

DTPPO: Dual-Transformer Encoder-Based Proximal Policy Optimization for Multi-UAV Navigation in Unseen Complex Environments

Existing multi-agent deep reinforcement learning (MADRL) methods for multi-UAV navigation face challenges in generalization, particularly when applied to unseen complex environments. To address these limitations, we propose a Dual-Transformer Encoder-Based Proximal Policy Optimization (<i>DTPP...

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Bibliographic Details
Main Authors: Anning Wei, Jintao Liang, Kaiyuan Lin, Ziyue Li, Rui Zhao
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Drones
Subjects:
multi-UAV navigation
partially observable Markov decision process
multi-agent deep reinforcement learning
cross-scenario transferability
Online Access:https://www.mdpi.com/2504-446X/8/12/720
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