Uncovering reward goals in distributed drone swarms using physics-informed multiagent inverse reinforcement learning

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dc.contributor.authorPerrusquía, Adolfo
dc.contributor.authorGuo, Weisi
dc.date.accessioned2025-02-27T11:10:58Z
dc.date.available2025-02-27T11:10:58Z
dc.date.freetoread2025-02-27
dc.date.issued2025-01-01
dc.date.pubOnline2024-11-13
dc.description.abstractThe cooperative nature of drone swarms poses risks in the smooth operation of services and the security of national facilities. The control objective of the swarm is, in most cases, occluded due to the complex behaviors observed in each drone. It is paramount to understand which is the control objective of the swarm, whilst understanding better how they communicate with each other to achieve the desired task. To solve these issues, this article proposes a physics-informed multiagent inverse reinforcement learning (PI-MAIRL) that: 1) infers the control objective function or reward function from observational data and 2) uncover the network topology by exploiting a physics-informed model of the dynamics of each drone. The combined contribution enables to understand better the behavior of the swarm, whilst enabling the inference of its objective for experience inference and imitation learning. A physically uncoupled swarm scenario is considered in this study. The incorporation of the physics-informed element allows to obtain an algorithm that is computationally more efficient than model-free IRL algorithms. Convergence of the proposed approach is verified using Lyapunov recursions on a global Riccati equation. Simulation studies are carried out to show the benefits and challenges of the approach.
dc.description.journalNameIEEE Transactions on Cybernetics
dc.format.extentpp. 14-23
dc.identifier.citationPerrusquía A, Guo W. (2025) Uncovering reward goals in distributed drone swarms using physics-informed multiagent inverse reinforcement learning. IEEE Transactions on Cybernetics, Volume 55, Issue 1, January 2025, pp. 14-23
dc.identifier.eissn2168-2275
dc.identifier.elementsID559341
dc.identifier.issn2168-2267
dc.identifier.issueNo1
dc.identifier.urihttps://doi.org/10.1109/tcyb.2024.3489967
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23525
dc.identifier.volumeNo55
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10752585
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDrone swarms
dc.subjectimitation learning
dc.subjectmultiagent inverse reinforcement learning (IRL)
dc.subjectnetwork topology
dc.subjectphysics-informed
dc.subjectreward function
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subjectBehavioral and Social Science
dc.subjectBasic Behavioral and Social Science
dc.titleUncovering reward goals in distributed drone swarms using physics-informed multiagent inverse reinforcement learning
dc.typeArticle
dc.type.subtypeJournal Article
dcterms.dateAccepted2024-10-30

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