Conservative Q-Improvement: Reinforcement Learning for an Interpretable
Decision-Tree Policy

Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy

2 July 2019

Pooyan Jamshidi

Papers citing "Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy"

14 / 14 papers shown

Title
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation Joo Seung Lee Malini Mahendra Anil Aswani OffRL 67 1 0 10 Jan 2025
Revealing the Learning Process in Reinforcement Learning Agents Through Attention-Oriented Metrics Charlotte Beylier Simon M. Hofmann Nico Scherf 26 0 0 20 Jun 2024
Explainable Deep Reinforcement Learning: State of the Art and Challenges G. Vouros XAI 50 77 0 24 Jan 2023
MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation Aaron M. Roth Jing Liang Ram D. Sriram Elham Tabassi Tianyi Zhou 32 1 0 19 Sep 2022
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees Zichuan Liu Zichuan Liu Zhi Wang Yuanyang Zhu Chunlin Chen 63 5 0 15 Sep 2022
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes Yishay Mansour Michal Moshkovitz Cynthia Rudin FAtt 34 3 0 09 Jun 2022
MAVIPER: Learning Decision Tree Policies for Interpretable Multi-Agent Reinforcement Learning Stephanie Milani Zhicheng Zhang Nicholay Topin Z. Shi Charles A. Kamhoua Evangelos E. Papalexakis Fei Fang OffRL 80 13 0 25 May 2022
Explainability in reinforcement learning: perspective and position Agneza Krajna Mario Brčič T. Lipić Juraj Dončević 34 27 0 22 Mar 2022
XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees Aaron M. Roth Jing Liang Tianyi Zhou 52 8 0 22 Apr 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 59 653 0 20 Mar 2021
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods Nicholay Topin Stephanie Milani Fei Fang Manuela Veloso OffRL 24 32 0 25 Feb 2021
Evolutionary learning of interpretable decision trees Leonardo Lucio Custode Giovanni Iacca OffRL 41 40 0 14 Dec 2020
On Explaining Decision Trees Yacine Izza Alexey Ignatiev Sasha Rubin FAtt 24 85 0 21 Oct 2020
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning Wenjie Shi Gao Huang Shiji Song Zhuoyuan Wang Tingyu Lin Cheng Wu SSL 28 18 0 16 Mar 2020