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Balancing Policy Improvement and Evaluation in Risk ...
Springer
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由 H Wakabayashi 著作2020被引用 5 次 — One of the satisficing reinforcement learning algorithms, commonly known as RS+GRC, reduces large search space by setting an aspiration level.
Balancing policy improvement and evaluation in Risk- ...
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由 若林洋尭 著作2020 — As a result of the verification, it is shown that the RS using the eligibility trace was able to correctly evaluate the degree of satisficing. 1. Introduction.
Balancing Policy Improvement and Evaluation in Risk ...
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Balancing Policy Improvement and Evaluation in Risk-Sensitive Satisficing Algorithm · Hiroaki Wakabayashi, Takumi Kamiya, Tatsuji Takahashi · Published in JSAI 9 ...
Hiroaki Wakabayashi
DBLP
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Balancing Policy Improvement and Evaluation in Risk-Sensitive Satisficing Algorithm. JSAI 2020: 175-182. [+][–]. 2010 – 2019. FAQ. see FAQ. What is the meaning ...
Advances in Artificial Intelligence
Springer
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由 K Yada 著作 — Balancing Policy Improvement and Evaluation in Risk-Sensitive Satisficing Algorithm. Hiroaki Wakabayashi, Takumi Kamiya, Tatsuji Takahashi. Pages 175-182.
Softsatisficing: Risk-sensitive softmax action selection
ScienceDirect.com
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由 T Kamiya 著作2022被引用 7 次 — The Risk-sensitive Satisficing (RS) model implements satisficing in the reinforcement learning framework through conversion of action values into gains (or ...
Social satisficing: Multi-agent reinforcement learning with ...
ScienceDirect.com
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由 D Uragami 著作2024被引用 1 次 — The risk-sensitive satisficing (RS) is a RL policy that realizes autonomous balancing between exploration and exploitation (Takahashi et al., 2016). The RS-value ...
Conference Programme
The Chinese University of Hong Kong
https://pomshk2025.cuhk.edu.hk › 2025/01 › Co...
The Chinese University of Hong Kong
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7 日前 — A Tail-Risk Sensitive Reinforcement. Learning Approach for Option ... Topic: Optimization & Algorithm for Future Supply Chain Management and ...
77 頁
Social Satisficing: Multi-agent reinforcement learning with ...
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2024年7月19日 — The risk-sensitive evaluation of action values by RS has been shown to be effective in reinforcement learning. In this paper, first we analyze ...
Risk-Sensitive Reinforcement Learning via Policy Gradient ...
arXiv
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arXiv
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由 M Fu 著作2018被引用 1 次 — This book surveys research on risk-sensitive RL that uses policy gradient search,. i.e., policy optimization in a stochastic formulation, as ...