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

2 July 2019

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

9 / 9 papers shown

Title
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation Joo Seung Lee Malini Mahendra Anil Aswani OffRL 53 1 0 10 Jan 2025
Revealing the Learning Process in Reinforcement Learning Agents Through Attention-Oriented Metrics Charlotte Beylier Simon M. Hofmann Nico Scherf 18 0 0 20 Jun 2024
Explainable Deep Reinforcement Learning: State of the Art and Challenges G. Vouros XAI 32 75 0 24 Jan 2023
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees Zichuan Liu Zichuan Liu Zhi Wang Yuanyang Zhu Chunlin Chen 42 5 0 15 Sep 2022
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes Yishay Mansour Michal Moshkovitz Cynthia Rudin FAtt 14 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 74 13 0 25 May 2022
Explainability in reinforcement learning: perspective and position Agneza Krajna Mario Brčič T. Lipić Juraj Dončević 23 26 0 22 Mar 2022
XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees Aaron M. Roth Jing Liang Dinesh Manocha 22 8 0 22 Apr 2021
On Explaining Decision Trees Yacine Izza Alexey Ignatiev João Marques-Silva FAtt 16 83 0 21 Oct 2020