Risk-sensitive reinforcement learning

We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference (TD) error, nonlinear transformations are effectively applied not only to the received reward...

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Bibliographic Details

Published in:	Neural computation Vol. 26; no. 7; p. 1298
Main Authors:	Shen, Yun, Tobia, Michael J, Sommer, Tobias, Obermayer, Klaus
Format:	Journal Article
Language:	English
Published:	United States 01.07.2014
Subjects:	Algorithms Brain - physiology Brain Mapping Decision Making - physiology Humans Magnetic Resonance Imaging Markov Chains Models, Psychological Nonlinear Dynamics Oxygen - blood Probability Reinforcement (Psychology) Risk
ISSN:	1530-888X, 1530-888X
Online Access:	Get more information
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