We investigate Monte Carlo based algorithms for solving stochastic control problems with local probabilistic constraints. Our motivation comes from microgrid management, where the controller tries to optimally dispatch a diesel generator while maintaining low probability of blackouts at each step. The key question we are empirical simulation procedures learning state-dependent admissible set th...