We study the model of projective simulation (PS), a novel approach to arti cial intelligence based
on stochastic processing of episodic memory which was recently introduced [1]. Here we provide a
detailed analysis of the model and examine its performance, including its achievable e ciency, its
learning times and the way both properties scale with the problems' dimension. In addition, we
situate the PS agent in di erent learning scenarios, and study its learning abilities. A variety of
new scenarios are being considered, thereby demonstrating the model's
exibility. Further more, to
put the PS scheme in context, we compare its performance with those of Q-learning and learning
classi er systems, two popular models in the eld of reinforcement learning. It is shown that PS is
a competitive arti cial intelligence model of unique properties and strengths.Austrian Science Fund (FWF) SFB FoQuS F4012Templeton World Charity Foundation (TWCF
