Projective simulation applied to the grid-world and the mountain-car problem

Abstract

We study the model of projective simulation (PS) which is a novel approach to artificial intelligence (AI). Recently it wasshown that the PS agent performs well in a number of simple task environments, also when compared to standard models ofreinforcement learning (RL). In this paper we study the performance of the PS agent further in more complicated scenarios. Tothat end we chose two well-studied benchmarking problems, namely the “grid-world” and the “mountain-car” problem, whichchallenge the model with large and continuous input space. We compare the performance of the PS agent model with those ofexisting models and show that the PS agent exhibits competitive performance also in such scenarios.