UAV Collision Avoidance in Unknown Scenarios with Causal Representation Disentanglement

Deep reinforcement learning (DRL) has significantly advanced online path planning for unmanned aerial vehicles (UAVs). Nonetheless, DRL-based methods often encounter reduced performance when dealing with unfamiliar scenarios. This decline is mainly caused by the presence of non-causal and domain-spe...

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Auteurs principaux:	Zhun Fan, Zihao Xia, Che Lin, Gaofei Han, Wenji Li, Dongliang Wang, Yindong Chen, Zhifeng Hao, Ruichu Cai, Jiafan Zhuang
Format:	Artigo
Langue:	Inglês
Publié:	MDPI AG 2024-12-01
Collection:	Drones
Sujets:	deep reinforcement learning unmanned aerial vehicles generalization capability policy learning causal representation disentanglement
Accès en ligne:	https://www.mdpi.com/2504-446X/9/1/10
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