A Generative Hyper-Heuristic based on Multi-Objective Reinforcement Learning: the UAV Swarm Use Case

The interest in Unmanned Aerial Vehicles (UAVs) for civilian applications has seen a drastic increase in the past few years. Indeed, UAVs feature unique properties such as three-dimensional mobility and payload flexibility which provide unprecedented advantages when conducting missions like infrastructure inspection or search and rescue. However their current usage is mainly limited to a single operated or autonomous device which brings several limitations like its range of action and resilience. Using several UAVs as a swarm is one promising approach to address those limitations. However, manually designing globally efficient swarming approaches that solely rely on distributed behaviours is a complex task. The goal of this work is thus to automate the design of UAV swarming behaviours to tackle an area coverage problem. The first contribution of this work consists in modelling this problem as a multi-objective optimisation problem. The second contribution is a hyper-heuristic based on multi-objective reinforcement learning for generating distributed heuristics for that problem. Experimental results demonstrate the good stability of the generated heuristic on instances with different sizes and its capacity to well balance the multiple objectives of the optimisation problem.