Refinement of robot motor skills through reinforcement learning

An extension of earlier work in the refinement of robotic motor control using reinforcement learning is described. It is no longer assumed that the magnitude of the state-dependent nonlinear torque is known. The learning controller learns about not only the presence of the torque, but also its magnitude. The ability of the learning system to learn this real-valued mapping from output feedback and reference input to control signal is facilitated by a stochastic algorithm that uses reinforcement feedback. A learning controller that can learn nonlinear mappings holds many possibilities for extending existing adaptive control research.<
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