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Reinforcement Learning for Visual Servoing of a Mobile Robot

By Chris Gaskett, Luke Fletcher and Alexander Zelinsky


A novel reinforcement learning algorithm is applied to a visual servoing task on a real mobile robot. There is no requirement for camera calibration, an actuator model or a knowledgeable teacher. The controller learns from a critic which gives a scalar reward. The learning algorithm handles continuously valued states and actions and can learn from good and bad experiences including data gathered while performing unrelated behaviours and from historical data. Experimental results are presented

Year: 2000
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