• Corpus ID: 50219894

Deep Reinforcement Learning for Accelerating the Convergence Rate

@inproceedings{Fu2016DeepRL,
  title={Deep Reinforcement Learning for Accelerating the Convergence Rate},
  author={Jie Fu and Zichuan Lin and Danlu Chen and Ritchie Ng and Miao Liu and Nicholas L{\'e}onard and Jiashi Feng and Tat-Seng Chua},
  year={2016}
}
In this paper, we propose a principled deep reinforcement learning (RL) approach that is able to accelerate the convergence rate of general deep neural networks (DNNs. [] Key Method The state features of the agent are learned from the weight statistics of the optimizee during training. The reward function of this agent is designed to learn policies that minimize the optimizee’s training time given a certain performance goal.

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