Before revision:  Addressing these issues  , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant. Both these interpolation variants operate locally, while  implicitly accounting for global spatial relationships in the entire system through recursion.
After revision:  Addressing these issues , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) . All variants of MRP interpolation operate locally, while implicitly accounting for global spatial relationships in the entire system through recursion.