Titlebook: Handbook of Markov Decision Processes; Methods and Applicat Eugene A. Feinberg,Adam Shwartz Book 2002 Springer Science+Business Media New Y

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Introductionective area. The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic notation and concepts of Section 1.2. Most chap- ters should be accessible by graduate or advanced undergraduate stude

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Finite State and Action MDPS the fifties. We consider finite and infinite horizon models. For the finite horizon model the utility function of the total expected reward is commonly used. For the infinite horizon the utility function is less obvious. We consider several criteria: total discounted expected reward, average expect

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Mixed Criteriaand average rewards as well as linear combinations of total discounted rewards with different discount factors are examples of mixed criteria. We discuss the structure of optimal policies and algorithms for their computation for problems with and without constraints.

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Invariant Gambling Problems and Markov Decision Processestationary plans are almost surely adequate for a leavable, measurable, invariant gambling problem with a nonnegative utility function and a finite optimal reward function. This generalizes results about stationary plans for positive Markov decision models as well as measurable gambling problems.

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