mlsanityData to 7 Oct 2026

Over-the-air Federated Policy Gradient

arXiv 2310.16592 ↗First posted 25 Oct 2023cs.LG cs.DC eess.SPcited by 0

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Abstract (arXiv)

In recent years, over-the-air aggregation has been widely considered in large-scale distributed learning, optimization, and sensing. In this paper, we propose the over-the-air federated policy gradient algorithm, where all agents simultaneously broadcast an analog signal carrying local information to a common wireless channel, and a central controller uses the received aggregated waveform to update the policy parameters. We investigate the effect of noise and channel distortion on the convergence of the proposed algorithm, and establish the complexities of communication and sampling for finding an ε-approximate stationary point. Finally, we present some simulation results to show the effectiveness of the algorithm.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 29 references that are in the map. First posted on arXiv 25 Oct 2023.

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14 of 29 references.

References · 14

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 301
  3. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 257
  4. 2018Federated Learning via Over-the-Air Computationcited by 215
  5. 2019Federated Learning over Wireless Fading Channelscited by 178
  6. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 108
  7. 2018Stochastic Variance-Reduced Policy Gradientcited by 103
  8. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  9. 2019A Survey of Deep Reinforcement Learning in Video Gamescited by 73
  10. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
  11. 2022Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learningcited by 27
  12. 2021The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communicationcited by 5
2 more
  1. 2023Over-the-Air Multi-View Pooling for Distributed Sensingcited by 5
  2. 2021Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learningcited by 1

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