Markov Decision Processes with a New Optimality Criterion.
STANFORD UNIV CALIF DEPT OF OPERATIONS RESEARCH
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A Markov decision process can be characterized by specifying the following three elements a Markov process on which a return function and decision structure is placed, an objective function or optimality criterion, and a class of allowable policies or controls. For a given Markov decision process with these three elements suitably defined, the standard problems to investigate are the following The existence of a policy, within the class of allowable policies, which attains the maximal value of the objective function The fact that the optimal policy has a simple form The construction of a finite algorithm to compute the optimal policy. The report discusses these problems for standard Markov decision processes with a new optimality criterion. Author
- Operations Research