Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks

• URL: http://arxiv.org/abs/2306.02029v2
• Date: Sat, 7 Oct 2023 07:50:35 GMT
• Title: Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
• Authors: Jichao Chen, Omid Esrafilian, Harald Bayerlein, David Gesbert, and Marco Caccamo
• Abstract summary: We propose a novel model-aided federated MARL algorithm to coordinate multiple UAVs on a data harvesting mission with only limited knowledge about the environment. A performance comparison with standard MARL algorithms demonstrates that our proposed model-aided FedQMIX algorithm reduces the need for real-world training experiences by around three magnitudes.
• Score: 17.770665737751372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying teams of unmanned aerial vehicles (UAVs) to harvest data from distributed Internet of Things (IoT) devices requires efficient trajectory planning and coordination algorithms. Multi-agent reinforcement learning (MARL) has emerged as a solution, but requires extensive and costly real-world training data. To tackle this challenge, we propose a novel model-aided federated MARL algorithm to coordinate multiple UAVs on a data harvesting mission with only limited knowledge about the environment. The proposed algorithm alternates between building an environment simulation model from real-world measurements, specifically learning the radio channel characteristics and estimating unknown IoT device positions, and federated QMIX training in the simulated environment. Each UAV agent trains a local QMIX model in its simulated environment and continuously consolidates it through federated learning with other agents, accelerating the learning process. A performance comparison with standard MARL algorithms demonstrates that our proposed model-aided FedQMIX algorithm reduces the need for real-world training experiences by around three magnitudes while attaining similar data collection performance.

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