Doubly Robust Off-Policy Value Evaluation for Reinforcement Learning. Jiang, N. & Li, L. In International Conference on Machine Learning, pages 652-661.
Doubly Robust Off-Policy Value Evaluation for Reinforcement Learning [link]Paper  abstract   bibtex   
We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy. This problem ...
@inproceedings{jiangDoublyRobustOffpolicy2016,
  langid = {english},
  title = {Doubly {{Robust Off}}-Policy {{Value Evaluation}} for {{Reinforcement Learning}}},
  url = {http://proceedings.mlr.press/v48/jiang16.html},
  abstract = {We study the problem of off-policy value evaluation in reinforcement learning (RL), where one aims to estimate the value of a new policy based on data collected by a different policy. This problem ...},
  eventtitle = {International {{Conference}} on {{Machine Learning}}},
  booktitle = {International {{Conference}} on {{Machine Learning}}},
  urldate = {2019-05-17},
  date = {2016-06-11},
  pages = {652-661},
  author = {Jiang, Nan and Li, Lihong},
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}
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