A Minimum Principle for Decentralized Stochastic Control Problems
The notion of the “state” of a dynamical system has turned out, in retrospect, to be the single most important concept in system theory, and the representation of systems in state space form is now almost universally accepted as the correct framework for posing questions of control and for formulating and analyzing proposed solutions. The power of the state concept derives from two properties. First, the current value of the state is a sufficient (and “minimal”) statistic of all the past history of the system that is relevant for predicting future behavior. Second, the system’s optimal future behavior depends only on the current state, so it is a sufficient statistic for controlling future behavior in an optimal manner. As is well known, however, these properties can be exploited only when the state is directly observed—the so-called complete observation case—and so the utility of the state concept is reduced considerably when it can be observed only indirectly—the partial observation case.
KeywordsControl Input SIAM Journal Minimum Principle State Concept Learning Function
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