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HOW TO CHOOSE THE STATE RELEVANCE WEIGHT OF THE APPROXIMATE LINEAR PROGRAM?

The linear programming approach to approximate dynamic programming was introduced in [1]. Whereas the state relevance weight (i.e. the cost vector) of the linear program does not matter for exact dynamic programming, it is not the case for approximate dynamic programming. In this paper, we address the issue of selecting an appropriate state relevant weight in the case of approximate dynamic programming. In particular, we want to choose c so that there is a practical control of the approximate policy performance by the capability of the approximation architecture. We present here some theoretical results and more practical guidelines to select a good state relevance vector.