On Implicit Approximation of the Bellman Equation ?

In this article, an efficient algorithm for an optimal decision strategy approximation is introduced. It approximates the Bellman equation without omitting the principal uncertainty stemming from incomplete knowledge. Thus, the approximated optimal strategy retains the ability to constantly verify the current knowledge. An integral part of the proposed solution is a reduction in memory demands using HDMR approximation. The result of this method is a linear algebraic system for an approximated upper bound on the Bellman function. The analysis of the approximation error has not been considered here. One illustrative example has been completely resolved.