A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning

Bo Liu 0039, Xidong Feng, Jie Ren, Luo Mai, Rui Zhu, Haifeng Zhang 0002, Jun Wang 0012, Yaodong Yang 0001. A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning. In Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, A. Oh, editors, Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022. 2022. [doi]

@inproceedings{0039FRMZ00022,
  title = {A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning},
  author = {Bo Liu 0039 and Xidong Feng and Jie Ren and Luo Mai and Rui Zhu and Haifeng Zhang 0002 and Jun Wang 0012 and Yaodong Yang 0001},
  year = {2022},
  url = {http://papers.nips.cc/paper_files/paper/2022/hash/c8f9db5b83fac60ca3c6d6d06a9adcd6-Abstract-Conference.html},
  researchr = {https://researchr.org/publication/0039FRMZ00022},
  cites = {0},
  citedby = {0},
  booktitle = {Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022},
  editor = {Sanmi Koyejo and S. Mohamed and A. Agarwal and Danielle Belgrave and K. Cho and A. Oh},
  isbn = {9781713871088},
}