A novel optimal bipartite consensus control scheme for unknown multi-agent systems via model-free reinforcement learning

Zhinan Peng, Jiangping Hu, Kaibo Shi, Rui Luo, Rui Huang, Bijoy Kumar Ghosh, Jiuke Huang

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

In this paper, the optimal bipartite consensus control (OBCC) problem is investigated for unknown multi-agent systems (MASs) with coopetition networks. A novel distributed OBCC scheme is proposed based on model-free reinforcement learning method to achieve OBCC, where the agent's dynamics are no longer required. First, The coopetition networks are applied to establish the cooperative and competitive interactions among agents, and then the OBCC problem is formulated by introducing local neighbor bipartite consensus errors and performance index functions (PIFs) for each agent. Second, in order to obtain the OBCC laws, a policy iteration algorithm (PIA) is employed to learn the solutions to discrete-time (DT) Hamilton-Jacobi-Bellman (HJB) equations. Third, to implement the proposed methods, we adopt a data-driven actor-critic-based neural networks (NNs) framework to approximate the control laws and the PIFs, respectively, in an online learning manner. Finally, some simulation results are given to demonstrate the effectiveness of the developed approaches.

Original languageEnglish
Article number124821
JournalApplied Mathematics and Computation
Volume369
DOIs
StatePublished - Mar 15 2020

Keywords

  • Coopetition network
  • Model-free
  • Multi-agent systems
  • Optimal bipartite consensus control
  • Reinforcement learning

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