Cost-Sensitive Trees for Interpretable Reinforcement Learning-Reference-Cited by-同舟云学术

Cost-Sensitive Trees for Interpretable Reinforcement Learning

Published:2024-01-04 Issue: Volume:22 Page:91-99
ISSN:
Container-title:Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)
language:
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Author:

Nishtala Siddharth¹^ORCID,Ravindran Balaraman¹^ORCID

Affiliation:

1. Robert Bosch Centre for Data Science and AI, Indian Institute of Technology Madras, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3632410.3632443

Reference29 articles.

1. Osbert Bastani, Yewen Pu, and Armando Solar-Lezama. 2018. Verifiable Reinforcement Learning via Policy Extraction. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.). Vol. 31. Curran Associates, Inc.https://proceedings.neurips.cc/paper_files/paper/2018/file/e6d8545daa42d5ced125a4bf747b3688-Paper.pdf

2. Leo Breiman, Jerome Friedman, Charles J Stone, and RA Olshen. 1984. Classification and Regression Trees. CRC Press.

3. Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016. OpenAI Gym. CoRR abs/1606.01540 (2016). arXiv:1606.01540http://arxiv.org/abs/1606.01540

4. David Chapman and Leslie Pack Kaelbling. 1991. Input Generalization in Delayed Reinforcement Learning: An Algorithm and Performance Comparisons.. In Ijcai Vol. 91. 726–731.

5. Youri Coppens, Kyriakos Efthymiadis, Tom Lenaerts, and Ann Nowé. 2019. Distilling Deep Reinforcement Learning Policies in Soft Decision Trees. In International Joint Conference on Artificial Intelligence.

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