Corpus ID: 59222853

Federated Reinforcement Learning

@article{Zhuo2019FederatedRL,
  title={Federated Reinforcement Learning},
  author={Hankui Zhuo and Wenfeng Feng and Q. Xu and Qiang Yang and Yufeng Lin},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.08277}
}

Hankui Zhuo, Wenfeng Feng, +2 authors Yufeng Lin

Published 2019

Mathematics, Computer Science

ArXiv

In reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited. Directly transferring data or knowledge from an agent to another agent will not work due to the privacy requirement of data and models. In this paper, we propose a novel reinforcement learning approach to considering the privacy requirement and building Q-network for each agent with the help of other agents, namely federated reinforcement… CONTINUE READING

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