Designing Interpretable Approximations to Deep Reinforcement Learning

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Designing Interpretable Approximations to Deep Reinforcement Learning

28 October 2020

K. C. Kalagarla

Pierluigi Nuzzo

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Papers citing "Designing Interpretable Approximations to Deep Reinforcement Learning"

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Title
Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning Xiao Liu Wubing Chen Mao Tan 130 2 0 12 Sep 2023
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information V. Sharma John P. Dickerson Erfaun Noorani AI4CE 80 0 0 09 Nov 2022
ProtoX: Explaining a Reinforcement Learning Agent via PrototypingNeural Information Processing Systems (NeurIPS), 2022 Ronilo Ragodos Tong Wang Qihang Lin Xun Zhou 123 9 0 06 Nov 2022
Keeping Minimal Experience to Achieve Efficient Interpretable Policy Distillation Xiao Liu Shuyang Liu Wenbin Li Shangdong Yang Yang Gao OffRL 92 0 0 02 Mar 2022
Interpretable Machine Learning: Fundamental Principles and 10 Grand ChallengesStatistics Survey (Stat. Surv.), 2021 Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 339 809 0 20 Mar 2021
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation M. Amgad Lamees A. Atteya Hagar Hussein K. Mohammed Ehab Hafiz ... Critical Care David Manthey Atlanta D. Neurology Lurie Cancer Center 118 88 0 18 Feb 2021