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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Vydané v:	Neural computation Ročník 26; číslo 7; s. 1298
Hlavní autori:	Shen, Yun, Tobia, Michael J, Sommer, Tobias, Obermayer, Klaus
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 01.07.2014
Predmet:	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
On-line prístup:	Zistit podrobnosti o prístupe
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