This paper has later been selected for publication in the International Journal of Tomography and Statistics (IJTS). Note that paper "Reinforcement learning versus model predictive control: a comparison a power system problem" is an extended and more mature version of this work.
model predictive control; reinforcement learning; interior-point method; fitted Q iteration
Abstract :
[en] Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a discrete-time optimal control problem and compute a suboptimal control policy. We present in this paper in a unified framework these two families of methods. We run for MPC and RL algorithms simulations on a benchmark control problem taken from the power system literature and discuss the results obtained.
