Risk-sensitive Policy Learning
Risk-Sensitive Policy Learning is a methodological framework within policy learning that explicitly incorporates risk measures—such as variance, conditional value-at-risk, or entropic risk—into the policy optimization process to prioritize robustness and caution under uncertainty, distinguishing it from risk-neutral approaches by its sensitivity to the distributional characteristics of outcomes.
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2017–2026
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James G. March · Psychological Review · 1996 · 306 citations
Social Sciences, Behavioral Sciences, Behavioral Economics +11
Risk-Sensitive Reinforcement Learning
Oliver Mihatsch, Ralph Neuneier · Machine Learning · 2002 · 262 citations · Full text
Risk-sensitive reinforcement learning applied to control under constraints
2005 · 204 citations
Risk-Sensitive Reinforcement Learning
Neural Computation · 2014 · 113 citations · Full text
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Technion – Israel Institute of Technology
Haifa, Israel
3 h-index
7 publications
551 citations
Princeton University
Princeton, United States
3 h-index
4 publications
54 citations
Stanford University
Stanford, United States
3 h-index
4 publications
413 citations

DEVCOM Army Research Laboratory
Adelphi, United States
2 h-index
4 publications
46 citations

Tata Institute of Fundamental Research
Mumbai, India
2 h-index
2 publications
136 citations
Technische Universität Berlin
Berlin, Germany
2 h-index
2 publications
308 citations
University of Maryland, College Park
College Park, United States
2 h-index
1 publications
22 citations
Universität Hamburg
Hamburg, Germany
1 h-index
2 publications
226 citations
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Repository · 6 publications · 133 citations
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2 publications · 329 citations
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1 publications · 28 citations
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IJRR · 1 publications · 37 citations
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1 publications · 26 citations
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