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risk sensitive reinforcement learning

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Risk Sensitive Reinforcement Learning

Neural Information Processing SystemsApr-6-2023, 17:23:27 GMT

A directed generative model for binary data using a small number of hidden continuous units is investigated. The relationships between the correlations of the underly(cid:173) ing continuous Gaussian variables and the binary output variables are utilized to learn the appropriate weights of the network. The advantages of this approach are illustrated on a translationally in(cid:173) variant binary distribution and on handwritten digit images.

cid, risk sensitive reinforcement learning

Neural Information Processing Systems

Technology: Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.40)

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