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Reinforcement Learning (RL) is an intuitive way of programming well-suited for use on autonomous robots because it does not need to specify how the task has to be achieved. However, RL remains difficult to implement in realistic domains because of its slowness in convergence. In this paper, we develop a theoretical study of the influence of some RL parameters over the learning speed. We also provide experimental justifications for choosing the reward function and initial Q-values in order todoi:10.1109/iros.2006.282341 dblp:conf/iros/MatignonLF06 fatcat:xnhnia3uyzdsrofkmiuseaysia