Reinforcement learning algorithm for multirobot will become very slow when the number of robots is increasing resulting in an\nexponential increase of state space. A sequential Q-learning based on knowledge sharing is presented.The rule repository of robots\nbehaviors is firstly initialized in the process of reinforcement learning.Mobile robots obtain present environmental state by sensors.\nThen the state will be matched to determine if the relevant behavior rule has been stored in the database. If the rule is present, an\naction will be chosen in accordance with the knowledge and the rules, and the matching weight will be refined. Otherwise the new\nrule will be appended to the database. The robots learn according to a given sequence and share the behavior database.We examine\nthe algorithm by multirobot following-surrounding behavior, and find that the improved algorithm can effectively accelerate the\nconvergence speed.
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