Multistage Markov Decision Processes with Minimum Criteria of Random Rewards
نویسندگان
چکیده
We consider multistage decision processes where criterion function is an expectation of minimum function. We formulate them as Markov decision processes with imbedded parameters. The policy depends upon a history including past imbedded parameters, and the rewards at each stage are random and depend upon current state, action and a next state. We then give an optimality equation by using operators and show that there exists a right continuous deterministic Markov policy, which depends upon a current state and an imbedded parameter.
منابع مشابه
Chapter for MARKOV DECISION PROCESSES
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