Corpus ID: 199442398

Deep Reinforcement Learning in System Optimization

@article{HajAli2019DeepRL,
  title={Deep Reinforcement Learning in System Optimization},
  author={Ameer Haj-Ali and Nesreen Ahmed and Theodore L. Willke and Joseph Gonzalez and Krste Asanovi{\'c} and Ion Stoica},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.01275}
}
The recent advancements in deep reinforcement learning have opened new horizons and opportunities to tackle various problems in system optimization. Such problems are generally tailored to delayed, aggregated, and sequential rewards, which is an inherent behavior in the reinforcement learning setting, where an agent collects rewards while exploring and exploiting the environment to maximize the long term reward. However, in some cases, it is not clear why deep reinforcement learning is a good… Expand
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