Provably Efficient Adversarial Imitation Learning with Unknown Transitions. Xu, T., Li, Z., Yu, Y., & Luo, Z. In Evans, R. J. & Shpitser, I., editors, Uncertainty in Artificial Intelligence, UAI 2023, July 31 - 4 August 2023, Pittsburgh, PA, USA, volume 216, of Proceedings of Machine Learning Research, pages 2367–2378, 2023. PMLR.
Provably Efficient Adversarial Imitation Learning with Unknown Transitions [link]Paper  bibtex   
@inproceedings{DBLP:conf/uai/XuL0L23,
  author       = {Tian Xu and
                  Ziniu Li and
                  Yang Yu and
                  Zhi{-}Quan Luo},
  editor       = {Robin J. Evans and
                  Ilya Shpitser},
  title        = {Provably Efficient Adversarial Imitation Learning with Unknown Transitions},
  booktitle    = {Uncertainty in Artificial Intelligence, {UAI} 2023, July 31 - 4 August
                  2023, Pittsburgh, PA, {USA}},
  series       = {Proceedings of Machine Learning Research},
  volume       = {216},
  pages        = {2367--2378},
  publisher    = {{PMLR}},
  year         = {2023},
  url          = {https://proceedings.mlr.press/v216/xu23c.html},
  timestamp    = {Mon, 28 Aug 2023 17:23:08 +0200},
  biburl       = {https://dblp.org/rec/conf/uai/XuL0L23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

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