DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 방효충 | - |
dc.contributor.author | Im, Sukjae | - |
dc.contributor.author | 임석재 | - |
dc.date.accessioned | 2024-08-08T19:30:28Z | - |
dc.date.available | 2024-08-08T19:30:28Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1097646&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321840 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 항공우주공학과, 2024.2,[ii, 38 p. :] | - |
dc.description.abstract | This paper explores defense methodologies based on reinforcement learning in a target defense game. The scenario involves a defending aircraft seeking to protect a target from an attacker. We assume the attacker is a fixed-wing vehicle with a speed advantage, while the defender is a slower multirotor aircraft capable of varying its flight speed and agile turns. In this context, the reinforcement learning agent develops a guidance strategy that capitalizes on the maneuverability differences between the attacker and the defender. The paper discusses strategies such as reward shaping to ensure stable convergence of the agent. Simulations, considering various performance and strategies of attacking aircraft, demonstrate the feasibility and success of the proposed reinforcement learning-based approach. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 심층 강화 학습▼a목표물 방어 게임▼a무인기 | - |
dc.subject | Deep reinforcement learning▼aTarget defense game▼aUnmanned aerial vehicle | - |
dc.title | (A) study on target defense game using deep reinforcement learning in the context of unmanned aerial vehicle | - |
dc.title.alternative | 심층 강화학습을 이용한 목표물 방어 게임에서의 무인기 방어 기법 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :항공우주공학과, | - |
dc.contributor.alternativeauthor | Bang, Hyochoong | - |
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