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This paper presents a novel Fuzzy Sarsa(λ) Learning (FSλL) approach applied to a strategic route leaning task of a mobile robot. FSλL is a hybrid architecture that combines Reinforcement Learning and Fuzzy Logic control. The Sarsa(λ) Learning algorithm is used to tune the rule-base of a Fuzzy Logic controller which has been tested in a route learning task. The robot explores its environment using its fixed experience provided by a discretized Fuzzy Logic controller, and then learns optimal policies to achieve goals in less time and less error.doi:10.1109/iros.2006.282215 dblp:conf/iros/TheodoridisH06 fatcat:ztutfdpoizawnpj6wssgvkmdgu