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June 19, 2026IEEE Transactions on Neural Networks and Learning Systems

Online Value Iteration for Unknown Nonlinear Multiagent Systems: A Model-Decoupled Encoding–Decoding Mechanism

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Authors

TZTong ZhangYHYiyan HanLYLe You

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Overview

Online trial develops optimal policies in nonlinear multiagent systems, suggesting enhanced consensus control methods.

Key Points

  • This research aims to create an effective control strategy for multiagent systems taking into account uncertainties from unknown nonlinear dynamics.
  • Proposed an online reinforcement learning control strategy based on a model-decoupled dynamic encoding-decoding mechanism.
  • Designed an online identifier to compensate for dynamic uncertainties during the encoding-decoding process.
  • Developed a distributed value iteration algorithm using solely decoded state information.
  • The proposed method ensures quantizer nonsaturation under uncertain dynamics, improving communication reliability.
  • Simulation studies on UAV-UGV formations demonstrated the robustness of the proposed control strategy.
  • Achieved optimal consensus control without reliance on neighboring control policies.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a34dc5265a5b0777af2ca20https://doi.org/10.1109/tnnls.2026.3700775
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