@misc{10481/77989,
year = {2014},
month = {12},
url = {https://hdl.handle.net/10481/77989},
abstract = {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.},
organization = {Austrian Science Fund (FWF)	SFB FoQuS F4012},
organization = {Templeton World Charity Foundation (TWCF)},
publisher = {Springer},
keywords = {Inteligencia artificial},
keywords = {Artificial intelligence},
title = {Projective simulation for classical learning agents: a comprehensive investigation},
author = {Mautner, Julian and Manzano Diosdado, Daniel},
}