Online meta-learning by parallel algorithm competition

@article{Elfwing2018OnlineMB,
  title={Online meta-learning by parallel algorithm competition},
  author={Stefan Elfwing and Eiji Uchibe and Kenji Doya},
  journal={ArXiv},
  year={2018},
  volume={abs/1702.07490}
}
The efficiency of reinforcement learning algorithms depends critically on a few meta-parameters that modulate the learning updates and the trade-off between exploration and exploitation. The adaptation of the meta-parameters is an open question, which arguably has become a more important issue recently with the success of deep reinforcement learning. The long learning times in domains such as Atari 2600 video games makes it not feasible to perform comprehensive searches of appropriate meta… CONTINUE READING
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