Proposal of Deterministic Policy Gradient Method for Port-Hamiltonian Systems Using Eligibility Trace and Verification by Numerical Experiment

Authors

Shuichi FUKUNAGA, Ryota KOKUBO

Abstract

No available

Citation

• Journal: Transactions of the Society of Instrument and Control Engineers
• Year: 2023
• Volume: 59
• Issue: 4
• Pages: 232–234
• Publisher: The Society of Instrument and Control Engineers
• DOI: 10.9746/sicetr.59.232

BibTeX

@article{FUKUNAGA_2023,
  title={{Proposal of Deterministic Policy Gradient Method for Port-Hamiltonian Systems Using Eligibility Trace and Verification by Numerical Experiment}},
  volume={59},
  ISSN={1883-8189},
  DOI={10.9746/sicetr.59.232},
  number={4},
  journal={Transactions of the Society of Instrument and Control Engineers},
  publisher={The Society of Instrument and Control Engineers},
  author={FUKUNAGA, Shuichi and KOKUBO, Ryota},
  year={2023},
  pages={232--234}
}

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References

• Nian, R., Liu, J. & Huang, B. A review On reinforcement learning: Introduction and applications in industrial process control. Computers & Chemical Engineering 139, 106886 (2020) – 10.1016/j.compchemeng.2020.106886
• Sprangers, O., Babuska, R., Nageshrao, S. P. & Lopes, G. A. D. Reinforcement Learning for Port-Hamiltonian Systems. IEEE Trans. Cybern. 45, 1017–1027 (2015) – 10.1109/tcyb.2014.2343194