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From Cooperation to Competition: Prediction and Planning in Constrained Multi-Agent Settings Using Data-Driven and Model-Based Optimal Control Methods
From Cooperation to Competition: Prediction and Planning in Constrained Multi-Agent Settings Using Data-Driven and Model-Based Optimal Control Methods
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152647
- ISBN
- 9798384454700
- DDC
- 621
- 저자명
- Zhu, Edward Liu.
- 서명/저자
- From Cooperation to Competition: Prediction and Planning in Constrained Multi-Agent Settings Using Data-Driven and Model-Based Optimal Control Methods
- 발행사항
- [Sl] : University of California, Berkeley, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 161 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Borrelli, Francesco.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2024.
- 초록/해제
- 요약As robotic systems become more advanced and their applications more complex, it is insufficient to consider a robot's behavior in isolation. Robots are increasingly expected to operate in environments populated with other intelligent agents. In order to perform their tasks well while obeying constraints, these robotic systems must be endowed with the ability to predict the behavior of the other agents in the environment in addition to planning their own actions over a time horizon. This is especially important in scenarios where agents do not communicate with each other and may behave in an adversarial manner. In this thesis, we investigate methods for prediction and planning for multi-agent systems over a variety of reward structures and information structures. Namely, we examine approaches which tackle the problem in cooperative, non-cooperative, and competitive scenarios where agents engage in partial or no communication about their intentions and future plans. These approaches are formulated through a combination of model-based optimal control and data-driven learning techniques, where we use data in a principled manner to construct or augment the objective and constraint functions of optimal control problems. This allows us to incorporate the rich and expressive behavior stemming from learned models in a transparent manner. We place a particular emphasis on the problem of autonomous racing, which is highly illustrative of competitive multi-agent settings with no agent communication and where both prediction and planning are paramount to achieving good performance while maintaining safety in the presence of adversarial agents. The presented approaches are evaluated in simulation and hardware experiments of vehicle navigation and racing tasks.
- 일반주제명
- Mechanical engineering
- 일반주제명
- Robotics
- 일반주제명
- Engineering
- 키워드
- Game theory
- 키워드
- Machine learning
- 키워드
- Optimal control
- 기타저자
- University of California, Berkeley Mechanical Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798384454700
■035 ▼a(MiAaPQ)AAI31486161
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a621
■1001 ▼aZhu, Edward Liu.
■24510▼aFrom Cooperation to Competition: Prediction and Planning in Constrained Multi-Agent Settings Using Data-Driven and Model-Based Optimal Control Methods
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a161 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Borrelli, Francesco.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2024.
■520 ▼aAs robotic systems become more advanced and their applications more complex, it is insufficient to consider a robot's behavior in isolation. Robots are increasingly expected to operate in environments populated with other intelligent agents. In order to perform their tasks well while obeying constraints, these robotic systems must be endowed with the ability to predict the behavior of the other agents in the environment in addition to planning their own actions over a time horizon. This is especially important in scenarios where agents do not communicate with each other and may behave in an adversarial manner. In this thesis, we investigate methods for prediction and planning for multi-agent systems over a variety of reward structures and information structures. Namely, we examine approaches which tackle the problem in cooperative, non-cooperative, and competitive scenarios where agents engage in partial or no communication about their intentions and future plans. These approaches are formulated through a combination of model-based optimal control and data-driven learning techniques, where we use data in a principled manner to construct or augment the objective and constraint functions of optimal control problems. This allows us to incorporate the rich and expressive behavior stemming from learned models in a transparent manner. We place a particular emphasis on the problem of autonomous racing, which is highly illustrative of competitive multi-agent settings with no agent communication and where both prediction and planning are paramount to achieving good performance while maintaining safety in the presence of adversarial agents. The presented approaches are evaluated in simulation and hardware experiments of vehicle navigation and racing tasks.
■590 ▼aSchool code: 0028.
■650 4▼aMechanical engineering
■650 4▼aRobotics
■650 4▼aEngineering
■653 ▼aGame theory
■653 ▼aMachine learning
■653 ▼aMulti-agent systems
■653 ▼aOptimal control
■653 ▼aVehicle navigation
■690 ▼a0548
■690 ▼a0771
■690 ▼a0537
■71020▼aUniversity of California, Berkeley▼bMechanical Engineering.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163275▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
