Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy. Roth, A. M., Topin, N., Jamshidi, P., & Veloso, M. CoRR, 2019. 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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