Generation of MPC-like Explicit Control Laws with Reinforcement Machine Learning

This research deals with synthesizing explicit optimal control laws with reinforcement machine-learning. We show how to construct a machine learning procedure, which learns the nearly optimal control action by successive applying random control action to the system and evaluating the response with respect to the MPC-like objective function and box constraints on control inputs and outputs.