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Adaptive autonomous control using online value iteration with gaussian processes
2009
2009 IEEE International Conference on Robotics and Automation
In this paper, we present a novel approach to controlling a robotic system online from scratch based on the reinforcement learning principle. In contrast to other approaches, our method learns the system dynamics and the value function separately, which permits to identify the individual characteristics and is, therefore, easily adaptable to changing conditions. The major problem in the context of learning control policies lies in high-dimensional state and action spaces, that needs to be
doi:10.1109/robot.2009.5152660
dblp:conf/icra/RottmannB09
fatcat:fcaxfbminfaarinom7bhusoooa