Experience Replay for Real-Time Reinforcement Learning Control

  title={Experience Replay for Real-Time Reinforcement Learning Control},
  author={Sander Adam and Lucian Busoniu and Robert Babuska},
  journal={IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)},
Reinforcement-learning (RL) algorithms can automatically learn optimal control strategies for nonlinear, possibly stochastic systems. A promising approach for RL control is experience replay (ER), which learns quickly from a limited amount of data, by repeatedly presenting these data to an underlying RL algorithm. Despite its benefits, ER RL has been studied only sporadically in the literature, and its applications have largely been confined to simulated systems. Therefore, in this paper, we… CONTINUE READING
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