Analysis of an Adaptive Control Scheme for a Partially Observed Controlled Markov Chain
Technical research rept.
MARYLAND UNIV COLLEGE PARK SYSTEMS RESEARCH CENTER
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The authors consider an adaptive finite state-controlled Markov chain with partial state information, motivated by a class of replacement problems. They present parameter estimation techniques based on the information available after actions that reset the state to a known value are taken. They prove that the parameter estimates converge w.p.1 to the true unknown parameter, under the feedback structure induced by a certainty equivalent adaptive policy. They also show that the adaptive policy is self-optimizing, in a long-run average sense, for any measurable sequence of parameter estimates converging w.p.1 to the true parameter.
- Numerical Mathematics
- Statistics and Probability
- Operations Research