Risk-aware Policy Optimization
Risk-Aware Policy Optimization is a decision-making framework in reinforcement learning that integrates risk-sensitive criteria into policy improvement, distinguishing itself by explicitly accounting for uncertainty and potential adverse outcomes in sequential decision processes under risk.
13
Publications
860
Citations
46
Authors
35
Institutions
Publications per year
2017–2026
10
13
Deep Reinforcement Learning for Optimizing Finance Portfolio Management
Yuh-Jong Hu, Shang-Jen Lin · 2019 · 61 citations
Computing, Computer Science, Finance +14
1–5 of 13
46
2
h-index
1
h-index
Hong Kong University of Science and Technology, Hong Kong SAR China
1 publications · 29 citations
1
h-index
1
h-index
1
h-index
1–5 of 46
35

Hong Kong Polytechnic University
Hong Kong, Hong Kong
2 h-index
2 publications
134 citations
Tsinghua University
Beijing, China
2 h-index
4 publications
754 citations
University of Michigan
Ann Arbor, United States
1 h-index
2 publications
26 citations

Karolinska University Hospital
Stockholm, Sweden
1 h-index
1 publications
99 citations

Hong Kong Science and Technology Parks Corporation
Shatin, Hong Kong
1 h-index
1 publications
30 citations

University of Macau
Macao, Macao
1 h-index
1 publications
104 citations
Georgia Institute of Technology
Atlanta, United States
1 h-index
1 publications
99 citations

University of Strathclyde
Glasgow, United Kingdom
1 h-index
1 publications
17 citations
1–8 of 35
2 publications · 123 citations
2
h-index
1 publications · 104 citations
1
h-index
1 publications · 286 citations
1
h-index
1 publications · 50 citations
1
h-index
1 publications · 17 citations
1
h-index
1–5