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

Authors

46

  1. XZ
    Xiaoge Zhang

    Vanderbilt University, United States

    2 publications · 134 citations

    2

    h-index

  2. SL
    Shang-Jen Lin

    National Chengchi University, Taiwan

    1 publications · 61 citations

    1

    h-index

  3. SS
    Shaojie Shen

    Hong Kong University of Science and Technology, Hong Kong SAR China

    1 publications · 29 citations

    1

    h-index

  4. JX
    Jianyu Xu

    Xi’an Jiaotong-Liverpool University, China

    1 publications · 17 citations

    1

    h-index

  5. YY
    Yifan Yang

    Tsinghua University, China

    1 publications · 286 citations

    1

    h-index

1–5 of 46

Venues

  1. 2

    h-index

  2. Decision Support Systems

    1 publications · 104 citations

    1

    h-index

  3. 1

    h-index

  4. European Journal of Operational Research

    1 publications · 50 citations

    1

    h-index

  5. IISE Transactions

    1 publications · 17 citations

    1

    h-index

1–5