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

Understand

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.