Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity

Kaiqing Zhang, Xiangyuan Zhang, Bin Hu, Tamer Basar. Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. pages 2949-2964, 2021. [doi]

@inproceedings{ZhangZHB21,
  title = {Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity},
  author = {Kaiqing Zhang and Xiangyuan Zhang and Bin Hu and Tamer Basar},
  year = {2021},
  url = {https://proceedings.neurips.cc/paper/2021/hash/1714726c817af50457d810aae9d27a2e-Abstract.html},
  researchr = {https://researchr.org/publication/ZhangZHB21},
  cites = {0},
  citedby = {0},
  pages = {2949-2964},
  booktitle = {Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual},
  editor = {Marc'Aurelio Ranzato and Alina Beygelzimer and Yann N. Dauphin and Percy Liang and Jennifer Wortman Vaughan},
}