Corpus ID: 59222853

Federated Reinforcement Learning

  title={Federated Reinforcement Learning},
  author={Hankui Zhuo and Wenfeng Feng and Q. Xu and Qiang Yang and Yufeng Lin},
  • 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
    21 Citations

    Figures, Tables, and Topics from this paper

    Explore Further: Topics Discussed in This Paper

    Federated Reinforcement Learning for Fast Personalization
    • 8
    Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits
    • PDF
    Federated LQR: Learning through Sharing
    • PDF
    Federated Reinforcement Learning for Training Control Policies on Multiple IoT Devices
    • 3
    • PDF
    Multi-Agent Visualization for Explaining Federated Learning
    • 3
    • PDF
    Federated Reinforcement Learning for Controlling Multiple Rotary Inverted Pendulums in Edge Computing Environments
    • 1
    Federated Reinforcement Learning for Automatic Control in SDN-based IoT Environments
    Performance Analysis and Optimization in Privacy-Preserving Federated Learning
    • 4
    • PDF
    Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing
    • PDF


    Transfer Learning for Reinforcement Learning Domains: A Survey
    • 1,194
    • PDF
    Federated Multi-Task Learning
    • 366
    • PDF
    Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
    • 897
    • PDF
    Multiagent cooperation and competition with deep reinforcement learning
    • 290
    • PDF
    Deep Learning with Differential Privacy
    • 1,415
    • PDF
    Communication-Efficient Learning of Deep Networks from Decentralized Data
    • 1,648
    • Highly Influential
    • PDF