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.

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

Guerra A.;Guidi F.;Dardari D.;
2021

Abstract

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.
BalkanCom 2021 : Wireless Future in the New Decade : Fourth International Balkan Conference on Communications and Networking
80
84
Guerra A.; Guidi F.; Dardari D.; Djuric P.M.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/863420
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