Joint Multi-Agent Reinforcement Learning and Message-Passing for Distributed Multi-Uav Network Management using Conflict Graphs

초록

This paper proposes a novel algorithm for distributed multi unmanned aerial vehicles (UAVs) cooperation in dynamic and unstable network environments by employing joint multi-agent reinforcement learning (MARL) and message-passing. To realize MARL, our proposed algorithm utilizes a centralized training with distributed execution (CTDE) framework. However, CTDE-based algorithms should be able to recognize the communications between UAVs and centralized server, which is not possible in every single time step. Therefore, after conducting centralized training for MARL, the distribution of the model for distributed execution should be re-designed. For this objective, a conflict graph-based approach is used, which enables graph-edge if two UAVs can talk to each other. Based on this conflict graph construction, message-passing is used to select UAVs for communication with the server. The non-selected UAVs can receive their models from conflict graph-connected UAVs.

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