Experience replay for least-squares policy iteration

@article{Liu2014ExperienceRF,
  title={Experience replay for least-squares policy iteration},
  author={Quan Liu and Xin Long Zhou and Fei Zhu and Qiming Fu and Yuchen Fu},
  journal={IEEE/CAA Journal of Automatica Sinica},
  year={2014},
  volume={1},
  pages={274-281}
}
Policy iteration, which evaluates and improves the control policy iteratively, is a reinforcement learning method. Policy evaluation with the least-squares method can draw more useful information from the empirical data and therefore improve the data validity. However, most existing online least-squares policy iteration methods only use each sample just once, resulting in the low utilization rate. With the goal of improving the utilization efficiency, we propose an experience replay for least… CONTINUE READING

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