Linear Quadratic Regulation using reinforcement learning
Simen Hagen, Ben Kröse · 1998
In this paper we describe a possible way to make reinforcement learning more applicable in the context of industrial manufacturing processes. We achieve this by formulating the optimization task in the linear quadratic regulation framework, for which a conventional control theoretic solution exist. By rewriting the Q-learning approach into a linear least squares approximation problem, we can make a fair comparison between the resulting approximation and that of the conventional system identification approach. Our experiment shows that the conventional approach performs slightly better. Also we can show that the amount of exploration noise, added during the generation of data, plays a crucial role in the outcome of both approaches. 1 Introduction Reinforcement Learning (RL) is a trial based method for optimization of the interaction with an environment or the control of a system [2][7]. The optimization is performed by approximating the future sum of evaluations and determine the feedb...