DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 성영철 | - |
dc.contributor.author | Kim, Jeonghye | - |
dc.contributor.author | 김정혜 | - |
dc.date.accessioned | 2024-07-30T19:31:22Z | - |
dc.date.available | 2024-07-30T19:31:22Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096786&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321568 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2024.2,[iv, 32 p. :] | - |
dc.description.abstract | In this paper, a unified framework for exploration in reinforcement learning (RL) is proposed based on an option-critic model. The proposed framework learns to integrate a set of diverse exploration strategies so that the agent can adaptively select the most effective exploration strategy over time to realize a relevant exploration-exploitation trade-off for each given task. The effectiveness of the proposed exploration framework is demonstrated by various experiments in the MiniGrid and Atari environments. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 탐색▼a탐색-이용 트레이드오프▼a옵션-크리틱 | - |
dc.subject | Exploration▼aExploitation-exploration trade-off▼aOption-critic | - |
dc.title | LESSON: Learning to integrate exploration strategies for reinforcement learning via an option framework | - |
dc.title.alternative | 옵션 프레임워크를 통한 강화 학습의 탐색 전략 통합 학습 알고리즘 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | Sung, Youngchul | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.