Сooperation of partially observed agents in ad-hoc open teams

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dc.contributor.advisorКузьменко, Дмитроuk_UA
dc.contributor.authorВiнокур, Євгенiйuk_UA
dc.date.accessioned2025-09-04T07:07:35Z
dc.date.available2025-09-04T07:07:35Z
dc.date.issued2025
dc.description.abstractThe aim of research: Systematic comparison of eight decentralized training baselines. We are inspired by the research of , where authors tested choosing best clearing action with Deep Learning on fire spread simulation. However, authors provide limited choice of algorithms with limited metrics and encounter non-stationairty issues due to common reward. Focus of our research is evaluation through extensive benchmarking of Independent, value-decomposition, central-critic, and agent-modeling methods proposed by Papoudakis et. al evaluated under common hardware/runtime constraints. Our work considers constraints of partial observability, generalization and mixed teams. Results promote insights on beneficiary features of baselines to assist further researches in selecting or developing effective algorithms for decen- tralized planning and control. Our contribution transfers Wildfire benchmark, created by Tran Research Group to PettingZoo library to promote verification of our results.en_US
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/36425
dc.language.isoen_USen_US
dc.statusfirst publisheden_US
dc.subjectdecentralized training baselinesen_US
dc.subjectDeep Learningen_US
dc.subjectalgorithms with limited metricsen_US
dc.subjectbenchmarkingen_US
dc.subjectPettingZoo libraryen_US
dc.subjectbachelor`s thesisen_US
dc.titleСooperation of partially observed agents in ad-hoc open teamsen_US
dc.title.alternativeCпiвпраця агентiв у спонтанних вiдкритих командах з неповних спостереженьuk_UA
dc.typeOtheren_US
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