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This paper proposes a distributed Multi-Agent Reinforcement Learning (MARL)algorithm for a team of Unmanned Aerial Vehicles (UAVs). The proposed MARLalgorithm allows UAVs to learn cooperatively to provide a full coverage of anunknown field of interest while minimizing the overlapping sections among theirfield of views. Two challenges in MARL for such a system are discussed in thepaper: firstly, the complex dynamic of the joint-actions of the UAV team, thatwill be solved using game-theoretic correlated equilibrium, and secondly, thechallenge in huge dimensional state space representation will be tackled withefficient function approximation techniques. We also provide our experimentalresults in detail with both simulation and physical implementation to show thatthe UAV team can successfully learn to accomplish the task.

Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage