Scalable Multi-Agent Reinforcement Learning for Dynamic Coordinated Multipoint Clustering

Fenghe Hu, Yansha Deng*, A. Hamid Aghvami

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Reinforcement learning (RL) is a widely investigated intelligent algorithm and proved to be useful in the wireless communication area. However, for optimization problems in large-scale multi-cell networks whose dimension increases exponentially, it is unrealistic to employ a conventional centralized RL algorithm and make decisions for the entire network. Multi-agent RL, which allows distribute decision-making, is expected to solve the scalability problem but with performance issues due to the unknown global information, i.e., non-stationary environment. In this paper, we propose a parameter-sharing multi-agent RL for grouping decisions of coordinated multi-point in a large-scale network, where agents jointly serve users to enhance the cell-edge service. By sharing information via parameters, our theoretical and simulation results show that parameter sharing can largely benefit the multi-agent algorithm with convergence proof and convergence speed analysis. To reduce the effect of biased local heterogeneous experience, we also propose a transfer learning method for the parameter sharing process, whose performance of transfer learning algorithms is verified by the simulation results.

Original languageEnglish
Pages (from-to)101-114
Number of pages14
JournalIEEE TRANSACTIONS ON COMMUNICATIONS
Volume71
Issue number1
DOIs
Publication statusPublished - 1 Jan 2023

Keywords

  • multi-agent systems
  • next generation networking
  • Reinforcement learning
  • telecommunication

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