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 has achieved remarkable progress in online path planning tasks for multi-UAV systems. However%2C existing DRL-based methods often suffer from performance degradation when tackling unseen scenarios%2C since the non-causal factors in visual representations adversely affect policy learning. To address this issue%2C we propose a novel representation learning approach%2C ie%2C causal representation disentanglement%2C which can identify the causal and non-causal factors in representations. After that%2C we only pass causal factors for subsequent policy learning and thus explicitly eliminate the influence of non-causal factors%2C which effectively improves the generalization ability of DRL models. Experimental results show that our proposed method can achieve robust navigation performance and effective collision avoidance especially in unseen scenarios%2C which significantly outperforms existing SOTA algorithms.?quality=80&w=800)
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