Efficient Reinforcement Learning Through Uncertainties- [electronic resource]
Efficient Reinforcement Learning Through Uncertainties- [electronic resource]
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101241
- ISBN
- 9798379721909
- DDC
- 004
- 저자명
- Zhou, Dongruo.
- 서명/저자
- Efficient Reinforcement Learning Through Uncertainties - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Los Angeles., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(167 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Gu, Quanquan.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약This dissertation is centered around the concept of uncertainty-aware reinforcement learning (RL), which seeks to enhance the efficiency of RL by incorporating uncertainty. RL is a vital mathematical framework in the field of artificial intelligence (AI) for creating autonomous agents that can learn optimal behaviors through interaction with their environments. However, RL is often criticized for being sample inefficient and computationally demanding. To tackle these challenges, the primary goals of this dissertation are twofold: to offer theoretical understanding of uncertainty-aware RL and to develop practical algorithms that utilize uncertainty to enhance the efficiency of RL.Our first objective is to develop an RL approach that is efficient in terms of sample usage for Markov Decision Processes (MDPs) with large state and action spaces. We present an uncertainty-aware RL algorithm that incorporates function approximation. We provide theoretical proof that this algorithm achieves near minimax optimal statistical complexity when learning the optimal policy. In our second objective, we address two specific scenarios: the batch learning setting and the rare policy switch setting. For both settings, we propose uncertainty-aware RL algorithms with limited adaptivity. These algorithms significantly reduce the number of policy switches compared to previous baseline algorithms while maintaining a similar level of statistical complexity. Lastly, we focus on estimating uncertainties in neural network-based estimation models. We introduce a gradient-based method that effectively computes these uncertainties. Our approach is computationally efficient, and the resulting uncertainty estimates are both valid and reliable.The methods and techniques presented in this dissertation contribute to the advancement of our understanding regarding the fundamental limits of RL. These research findings pave the way for further exploration and development in the field of decision-making algorithm design.
- 일반주제명
- Computer science.
- 키워드
- Machine learning
- 키워드
- Optimal policy
- 기타저자
- University of California, Los Angeles Computer Science 0201
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.