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A probabilistic analysis of bias optimality in unichain Markov decision processes

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2 Author(s)
Lewis, M.E. ; Dept. of Ind. & Oper. Eng., Michigan Univ., Ann Arbor, MI, USA ; Puterman, M.L.

Focuses on bias optimality in unichain, finite state, and action-space Markov decision processes. Using relative value functions, we present methods for evaluating optimal bias, this leads to a probabilistic analysis which transforms the original reward problem into a minimum average cost problem. The result is an explanation of how and why bias implicitly discounts future rewards

Published in:

Automatic Control, IEEE Transactions on  (Volume:46 ,  Issue: 1 )

Date of Publication:

Jan 2001

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