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- W2040672007 abstract "Research on robot techniques that are fast, user-friendly, and require little application-specific knowledge by the user, is more and more encouraged in a society where the demand of home-care or domestic-service robots is increasing continuously. In this context we propose a methodology which combines reinforcement learning and genetic algorithms to teach a robot how to perform a task when only the specification of the main restrictions of the desired behaviour is provided. Through this combination, both paradigms must be merged in such a way that they influence each other to achieve a fast convergence towards a good robot-control policy, and reduce the random explorations the robot needs to carry out in order to find a solution. Another advantage of our proposal is that it is able to easily incorporate any kind of domain-dependent knowledge about the task. This is very useful for improving a robot controller, for applying a robot-controller to move a different robot-platform, or when we have certain “feelings” about how the task should be solved. The performance of our proposal is shown through its application to solve a common problem in mobile robotics." @default.
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- W2040672007 date "2007-09-01" @default.
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- W2040672007 title "Autonomous and fast robot learning through motivation" @default.
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- W2040672007 doi "https://doi.org/10.1016/j.robot.2007.05.005" @default.
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