Multi-Agent Q-Learning in UAV Networks for Target Detection and Indoor Mapping

We consider a network of unmanned aerial vehicles (UAVs) for a search-and-rescue operations involving both detection of multiple targets and mapping of environment, where the learning time is limited. One possibility for accomplishing the goal while guaranteeing short learning time is to employ cooperation among UAVs. With this objective, we adopt a multi-agent Q-learning algorithm that allows the UAVs to learn a suitable navigation policy in real-time in order to complete a mission within a fixed time frame. The obtained results demonstrate that proper combination of the information gathered by the UAVs allows for an accelerated learning process.