The computational complexity implications of the iterative action filtering approach can be significant, especially for larger distribution systems with a large number of states and actions. As the number of states and actions increases, the number of iterations required to filter the applicable actions for each goal set also increases, leading to higher computational costs. To scale the approach to larger distribution systems, optimization techniques such as parallel computing, distributed computing, or heuristic algorithms can be employed to reduce the computational burden. Additionally, approximations or sampling methods can be used to speed up the filtering process while still maintaining a reasonable level of accuracy in the policy synthesis.