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Improving multi-UAV cooperative path-finding through multiagent experience learning
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DOI：10.1007/s10489-024-05771-w.png)
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Abstract
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A collaborators' experiences learning (CEL) algorithm, based on multiagent reinforcement learning (MARL) is presented for multi-UAV cooperative path-finding, where reaching destinations and avoiding obstacles are simultaneously considered as independent or interactive tasks. In this article, we are inspired by the experience learning phenomenon to propose the multiagent experience learning theory based on MARL. A strategy for updating parameters randomly is also suggested to allow homogeneous UAVs to effectively learn cooperative strategies. Additionally, the convergence of this algorithm is theoretically demonstrated. To demonstrate the effectiveness of the algorithm, we conduct experiments with different numbers of UAVs and different algorithms. The experiments show that the proposed method can achieve experience sharing and learning among UAVs and complete the cooperative path-finding task very well in unknown dynamic environments.
Keywords:
Collaborators' experience learning(CEL)
Multiagent reinforcement learning (MARL)
Cooperative path-finding
Random update order
Decentralized cooperation
UAVs
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