Efficient reinforcement learning for robots using informative simulated priors
IEEE International Conference on Robotics and Automation, pp. 2605-2612, 2015.
Autonomous learning through interaction with the physical world is a promising approach to designing controllers and decision-making policies for robots. Unfortunately, learning on robots is often difficult due to the large number of samples needed for many learning algorithms. Simulators are one way to decrease the samples needed from th...More
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