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Creators/Authors contains: "Kose, Umit"

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  1. ; (, Journal of machine learning research)
    null (Ed.)
    We propose a novel reinforcement learning methodology where the system performance is evaluated by a Markov coherent dynamic risk measure with the use of linear value function approximations. We construct projected risk-averse dynamic programming equations and study their properties. We propose new risk-averse counterparts of the basic and multi-step methods of temporal differences and we prove their convergence with probability one. We also perform an empirical study on a complex transportation problem. 
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