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Challenges in Continuous Path Planning: Rarefactions, Uncertainty and Reinforcement Learning- [electronic resource]
Challenges in Continuous Path Planning: Rarefactions, Uncertainty and Reinforcement Learning- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101516
- ISBN
- 9798380317986
- DDC
- 519
- 저자명
- Qi, Dongping.
- 서명/저자
- Challenges in Continuous Path Planning: Rarefactions, Uncertainty and Reinforcement Learning - [electronic resource]
- 발행사항
- [S.l.]: : Cornell University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(165 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- 주기사항
- Advisor: Vladimirsky, Alexander.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약We consider three optimal control problems, which focus on continuous path-planning applications, and each problem deals with a specific challenge. First, we introduce a new local factoring technique which can remove numerical artifacts arising from Eikonal equations with non-smooth conditions. Next, we deal with path-planning under an initial uncertainty, which can be removed later at some certainty time, and further discuss methods suitable for different notions of optimality. The third section considers an online-learning path-planning problem with unknown surveillance intensity and develops a Bayesian reinforcement learning method.In addition to the three path-planning problems, a new method of parameterizing neural networks is introduced. It follows the continuous optimal control interpretation of deep learning and uses B-spline basis functions to parameterize. For each problem we use numerical experiments to show the advantages of our proposed methods.
- 일반주제명
- Applied mathematics.
- 일반주제명
- Computer science.
- 키워드
- Optimal control
- 키워드
- Path planning
- 키워드
- Robustness
- 기타저자
- Cornell University Applied Mathematics
- 기본자료저록
- Dissertations Abstracts International. 85-03B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101516
■006m o d
■007cr#unu||||||||
■020 ▼a9798380317986
■035 ▼a(MiAaPQ)AAI30569263
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aQi, Dongping.▼0(orcid)0009-0003-4145-0518
■24510▼aChallenges in Continuous Path Planning: Rarefactions, Uncertainty and Reinforcement Learning▼h[electronic resource]
■260 ▼a[S.l.]:▼bCornell University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(165 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-03, Section: B.
■500 ▼aAdvisor: Vladimirsky, Alexander.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aWe consider three optimal control problems, which focus on continuous path-planning applications, and each problem deals with a specific challenge. First, we introduce a new local factoring technique which can remove numerical artifacts arising from Eikonal equations with non-smooth conditions. Next, we deal with path-planning under an initial uncertainty, which can be removed later at some certainty time, and further discuss methods suitable for different notions of optimality. The third section considers an online-learning path-planning problem with unknown surveillance intensity and develops a Bayesian reinforcement learning method.In addition to the three path-planning problems, a new method of parameterizing neural networks is introduced. It follows the continuous optimal control interpretation of deep learning and uses B-spline basis functions to parameterize. For each problem we use numerical experiments to show the advantages of our proposed methods.
■590 ▼aSchool code: 0058.
■650 4▼aApplied mathematics.
■650 4▼aComputer science.
■653 ▼aNumerical analysis
■653 ▼aOptimal control
■653 ▼aPath planning
■653 ▼aReinforcement learning
■653 ▼aRobustness
■690 ▼a0364
■690 ▼a0984
■690 ▼a0800
■71020▼aCornell University▼bApplied Mathematics.
■7730 ▼tDissertations Abstracts International▼g85-03B.
■773 ▼tDissertation Abstract International
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16934001▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
