A Survey on Policy Search for Robotics By

@inproceedings{Deisenroth2013ASO,
  title={A Survey on Policy Search for Robotics By},
  author={Marc Peter Deisenroth and Gerhard Neumann and Jan Peters},
  year={2013}
}
Policy search is a subfield in reinforcement learning which focuses on finding good parameters for a given policy parametrization. It is well suited for robotics as it can cope with high-dimensional state and action spaces, one of the main challenges in robot learning. We review recent successes of both model-free and model-based policy search in robot learning. Model-free policy search is a general approach to learn policies based on sampled trajectories. We classify model-free methods based… CONTINUE READING
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