  Deep reinforcement learning (DRL) has achieved remarkable progress in online
path planning tasks for multi-UAV systems. However, existing DRL-based methods
often suffer from performance degradation when tackling unseen scenarios, since
the non-causal factors in visual representations adversely affect policy
learning. To address this issue, we propose a novel representation learning
approach, \ie, causal representation disentanglement, which can identify the
causal and non-causal factors in representations. After that, we only pass
causal factors for subsequent policy learning and thus explicitly eliminate the
influence of non-causal factors, 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, which significantly outperforms existing SOTA
algorithms.
