Many reactive planning tasks are tackled by optimization combined with shrinking horizon at each time step: the problem is sim- plified to a non-reactive (myopic) optimization problem, based on the available information at the current time step and an estimate of future behavior, then it is solved; and the simplified problem is updated at each time step thanks to new information. This is in particular suitable when fast off-the-shelf components are available for the simplified problem - optimality stricto sensu is not possible, but good results are obtained at a reasonnable computational cost for highly untractable problems. As machines get more powerful, it makes sense however to go beyond the inherent limitations of this approach. Yet, a brute-force solving of the complete problem is often impossible; we here propose a methodology for embedding a solver inside a consistent reactive planning solver. Our methodology consists in embedding the solver in an Upper- Confidence-Tree algorithm, both in the nodes and as a Monte-Carlo simulator. We show the mathematical consistency of the approach, and then we apply it to a classical success of the myopic approach: the MineSweeper game.