Abstract
This paper studies the framework of a multi-agent system for the surveillance of zones. In such systems, patrolling agents are deployed to observe mobile targets. The observation problem consists in maximizing the number of viewed mobile targets by at least one agent of the mas. However, formal methods relying on potential field to solve this problem, such as the A-CMOMMT, that implement observation strategies, do not adapt them to the target’s behavior. In this article, we propose a trained method using a reinforcement learning (RL) approach to cope with naive and evasive targets in order to improve the observation of mobile targets, while protecting the patrolling agents from collisions. This paper compares our force field reinforcement learning (FFRL) method with some significant formal observation methods on various scenarios. It shows that FFRL has a better target’s observation than the studied methods for both naive and evasive targets.
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Chahal, J., Seghrouchni, A.E.F., Belbachir, A. (2022). A Force Field Reinforcement Learning Approach for the Observation Problem. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_9
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