평균 필드 게임 기반의 강화학습을 통한 무기-표적 할당Mean Field Game based Reinforcement Learning for Weapon-Target Assignment

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dc.contributor.author신민규ko
dc.contributor.author박순서ko
dc.contributor.author이단일ko
dc.contributor.author최한림ko
dc.date.accessioned2021-03-26T02:18:28Z-
dc.date.available2021-03-26T02:18:28Z-
dc.date.created2021-03-03-
dc.date.issued2020-08-
dc.identifier.citation한국군사과학기술학회지, v.23, no.4, pp.337 - 345-
dc.identifier.issn1598-9127-
dc.identifier.urihttp://hdl.handle.net/10203/281922-
dc.description.abstractThe Weapon-Target Assignment(WTA) problem can be formulated as an optimization problem that minimize the threat of targets. Existing methods consider the trade-off between optimality and execution time to meet the various mission objectives. We propose a multi-agent reinforcement learning algorithm for WTA based on mean field game to solve the problem in real-time with nearly optimal accuracy. Mean field game is a recent method introduced to relieve the curse of dimensionality in multi-agent learning algorithm. In addition, previous reinforcement learning models for WTA generally do not consider weapon interference, which may be critical in real world operations. Therefore, we modify the reward function to discourage the crossing of weapon trajectories. The feasibility of the proposed method was verified through simulation of a WTA problem with multiple targets in realtime and the proposed algorithm can assign the weapons to all targets without crossing trajectories of weapons.-
dc.languageKorean-
dc.publisher한국군사과학기술학회-
dc.title평균 필드 게임 기반의 강화학습을 통한 무기-표적 할당-
dc.title.alternativeMean Field Game based Reinforcement Learning for Weapon-Target Assignment-
dc.typeArticle-
dc.type.rimsART-
dc.citation.volume23-
dc.citation.issue4-
dc.citation.beginningpage337-
dc.citation.endingpage345-
dc.citation.publicationname한국군사과학기술학회지-
dc.identifier.kciidART002613144-
dc.contributor.localauthor최한림-
dc.description.isOpenAccessN-
dc.subject.keywordAuthor무기-표적 할당 문제-
dc.subject.keywordAuthor멀티 에이전트 강화학습-
dc.subject.keywordAuthor평균 필드 게임-
dc.subject.keywordAuthorWeapon-Target Assignment Problem-
dc.subject.keywordAuthorMulti-Agent Reinforcement Learning-
dc.subject.keywordAuthorMean Field Game-
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AE-Journal Papers(저널논문)
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