@article {Montesanto200684,
	title = {Navigation with memory in a partially observable environment},
	journal = {Robotics and Autonomous Systems},
	volume = {54},
	number = {1},
	year = {2006},
	note = {cited By 3},
	pages = {84-94},
	abstract = {The paper presents an architecture that allows the reactive visual navigation via an unsupervised reinforcement learning. This objective is reached using Q-learning and a hierarchical approach to the developed architecture. Using these techniques requires a deviation from the Partially Observable Markov Decision Processes (POMDP) and some innovations: heuristic techniques for generalizing the experience and for treating the partial observability; a technique for the speed adjournment of the Q function; the definition of a special reinforcement policy adequate for learning a complex task without supervision. The result is a satisfactory learning of the navigation assignment in a simulated environment. {\textcopyright} 2005 Elsevier B.V. All rights reserved.},
	doi = {10.1016/j.robot.2005.09.015},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-29344460057\&partnerID=40\&md5=a3f0d1229db6d5c8658cd509ca5acf81},
	author = {A. Montesanto and Guido Tascini and Paolo Puliti and Paola Baldassarri}
}
