In many application domains such as autonomous avionics, power electronics and process systems engineering there exist discretely controlled continuous processes (DCCPs) which constitute a special subclass of hybrid dynamical systems. We introduce a novel simulation-based approach for DDCPs optimization under uncertainty using Rein-forcement Learning with Gaussian Process models to learn the transitions dynamics descriptive of mode execution and an optimal switching policy for mode selection. Each mode implements a parameterized feedback control law until a stopping condition trig-gers. To deal with the size/dimension of the state space and a continuum of control mode parameters, Bayesian active learning is proposed using a utility function that trades off information content with policy improvement. Throughput maximization in a buffer tank subject to an uncertain schedule of sev-eral inflow discharges is used as case study address-ing supply chain control in manufacturing systemsFil: de Paula, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológio - CONICET - Santa Fe. Instituto de Desarrollo y Diseño (i); ArgentinaFil: Martinez, Ernesto Carlos. Consejo Nacional de Invest.cientif.y Tecnicas. Centro Cientifico Tecnol.conicet - Santa Fe. Instituto de Desarrollo y Dise?o (i)
