Abstract:
The choice of the control frequency of a system has a relevant impact on the ability of \emph{reinforcement learning algorithms} to learn a highly performing policy. In this paper, we introduce the notion of \emph{action persistence} that consists in the repetition of an action for a fixed number of decision steps, having the effect of modifying the control frequency. We start analyzing how action persistence affects the performance of the optimal policy, and then we present a novel algorithm, \emph{Persistent Fitted Q-Iteration} (PFQI), that extends FQI, with the goal of learning the optimal value function at a given persistence. After having provided a theoretical study of PFQI and a heuristic approach to identify the optimal persistence, we present an experimental campaign on benchmark domains to show the advantages of action persistence and proving the effectiveness of our persistence selection method.
