The Fuzzy Sars'a'(λ) Learning Approach Applied to a Strategic Route Learning Robot Behaviour

摘要

This paper presents a novel Fuzzy Sarsa(λ) Learning (FSλL) approach applied to a strategic route leaning task of a mobile robot. FSlambdaL 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.