Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy. Roth, A. M., Topin, N., Jamshidi, P., & Veloso, M. CoRR, 2019.
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy [link]Paper  bibtex   
@article{DBLP:journals/corr/abs-1907-01180,
  author       = {Aaron M. Roth and
                  Nicholay Topin and
                  Pooyan Jamshidi and
                  Manuela Veloso},
  title        = {Conservative Q-Improvement: Reinforcement Learning for an Interpretable
                  Decision-Tree Policy},
  journal      = {CoRR},
  volume       = {abs/1907.01180},
  year         = {2019},
  url          = {http://arxiv.org/abs/1907.01180},
  eprinttype    = {arXiv},
  eprint       = {1907.01180},
  timestamp    = {Mon, 08 Jul 2019 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1907-01180.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

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