REINFORCEMENT LEARNING USING GAUSSIAN PROCESSES FOR DISCRETELY CONTROLLED CONTINUOUS PROCESSES
Mariano De Paula, Ernesto Carlos Martinez · Americanae (AECID Library) · 2013
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 systems