Distributed Koopman learning using partial trajectories for control

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Abstract

This paper introduces distributed deep Koopman learning using partial trajectories for multi-agent dynamics learning. Each agent learns local Koopman dynamics from private trajectories and exchanges model estimates (not raw data) to reach consensus on a global model. Integrated with model predictive control, the learned model supports goal-tracking and station-keeping with good accuracy in simulation.

Demo

Demo coming soon.

Citation

Hao, Wenjian, Zehui Lu, Devesh Upadhyay, and Shaoshuai Mou. 2024. "Distributed Koopman Learning using Partial Trajectories for Control." arXiv preprint arXiv:2412.07212.

@techreport{WHao2024distribu,
  author = {W Hao, Z Lu, D Upadhyay, S Mou},
  year = {2024},
  title = {Distributed Koopman Learning using Partial Trajectories for Control},
  number = {arXiv:2412.07212},
  url = {https://arxiv.org/pdf/2412.07212}
}