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