Projective simulation applied to the grid-world and the mountain-car problem
This work addresses benchmarking challenges in AI for researchers, but it is incremental as it extends prior results to new problems.
The authors applied the projective simulation (PS) model to the grid-world and mountain-car problems, showing that it achieves competitive performance compared to existing reinforcement learning models in these more complex scenarios.
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.