Verifiable Reinforcement Learning via Policy Extraction

22 May 2018

Papers citing "Verifiable Reinforcement Learning via Policy Extraction"

50 / 167 papers shown

Title
Uniformly Conservative Exploration in Reinforcement Learning Wanqiao Xu Yecheng Jason Ma Kan Xu Hamsa Bastani Osbert Bastani OffRL 17 3 0 25 Oct 2021
Synthesizing Machine Learning Programs with PAC Guarantees via Statistical Sketching Osbert Bastani 28 0 0 11 Oct 2021
Adversarial Robustness Verification and Attack Synthesis in Stochastic Systems Lisa Oakley Alina Oprea S. Tripakis AAML 21 0 0 05 Oct 2021
ProTo: Program-Guided Transformer for Program-Guided Tasks Zelin Zhao Karan Samel Binghong Chen Le Song ViT LM&Ro 34 30 0 02 Oct 2021
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking Yizhen Dong Xingyu Zhao Xiaowei Huang 35 6 0 14 Sep 2021
Learning to Synthesize Programs as Interpretable and Generalizable Policies Dweep Trivedi Jesse Zhang Shao-Hua Sun Joseph J. Lim NAI 24 72 0 31 Aug 2021
Improving Human Sequential Decision-Making with Reinforcement Learning Hamsa Bastani Osbert Bastani W. Sinchaisri HAI OffRL 25 12 0 19 Aug 2021
Neural-to-Tree Policy Distillation with Policy Improvement Criterion Zhaorong Li Yang Yu Yingfeng Chen Ke Chen Zhipeng Hu Changjie Fan 31 5 0 16 Aug 2021
Unsupervised Learning of Neurosymbolic Encoders Eric Zhan Jennifer J. Sun Ann Kennedy Yisong Yue Swarat Chaudhuri 22 13 0 28 Jul 2021
Learning on Abstract Domains: A New Approach for Verifiable Guarantee in Reinforcement Learning Peng-yun Jin Min Zhang Jianwen Li Li Han Xuejun Wen OffRL 16 3 0 13 Jun 2021
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models Omid Davoodi Majid Komeili FAtt OffRL 24 6 0 14 May 2021
Rule-based Shielding for Partially Observable Monte-Carlo Planning Giulio Mazzi A. Castellini Alessandro Farinelli 13 14 0 28 Apr 2021
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
Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning Zikang Xiong Suresh Jagannathan 32 6 0 20 Apr 2021
Provable Repair of Deep Neural Networks Matthew Sotoudeh Aditya V. Thakur AAML 21 70 0 09 Apr 2021
A Review of Formal Methods applied to Machine Learning Caterina Urban Antoine Miné 46 55 0 06 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 59 655 0 20 Mar 2021
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning Zhihao Ma Yuzheng Zhuang Paul Weng Hankui Zhuo Dong Li Wulong Liu Jianye Hao NAI 51 14 0 15 Mar 2021
GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving Cillian Brewitt Balint Gyevnar Samuel Garcin Stefano V. Albrecht 18 30 0 10 Mar 2021
Generating Probabilistic Safety Guarantees for Neural Network Controllers Sydney M. Katz Kyle D. Julian Christopher A. Strong Mykel J. Kochenderfer 32 6 0 01 Mar 2021
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods Nicholay Topin Stephanie Milani Fei Fang Manuela Veloso OffRL 29 32 0 25 Feb 2021
Program Synthesis Guided Reinforcement Learning for Partially Observed Environments Yichen Yang J. Inala Osbert Bastani Yewen Pu Armando Solar-Lezama Martin Rinard 42 12 0 22 Feb 2021
SyReNN: A Tool for Analyzing Deep Neural Networks Matthew Sotoudeh Aditya V. Thakur AAML GNN 40 16 0 09 Jan 2021
Neurosymbolic Transformers for Multi-Agent Communication J. Inala Yichen Yang James Paulos Yewen Pu Osbert Bastani Vijay Kumar Martin Rinard Armando Solar-Lezama 32 24 0 05 Jan 2021
Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach Giulio Mazzi A. Castellini Alessandro Farinelli 26 9 0 23 Dec 2020
Demystifying Deep Neural Networks Through Interpretation: A Survey Giang Dao Minwoo Lee FaML FAtt 22 1 0 13 Dec 2020
CDT: Cascading Decision Trees for Explainable Reinforcement Learning Zihan Ding Pablo Hernandez-Leal G. Ding Changjian Li Ruitong Huang 12 21 0 15 Nov 2020
Designing Interpretable Approximations to Deep Reinforcement Learning Nathan Dahlin K. C. Kalagarla Nikhil Naik Rahul Jain Pierluigi Nuzzo 11 9 0 28 Oct 2020
Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action Systems Yashesh D. Dhebar Kalyanmoy Deb S. Nageshrao Ling Zhu Dimitar Filev 8 16 0 20 Sep 2020
Deep Learning & Software Engineering: State of Research and Future Directions P. Devanbu Matthew B. Dwyer Sebastian G. Elbaum M. Lowry Kevin Moran Denys Poshyvanyk Baishakhi Ray Rishabh Singh Xiangyu Zhang 11 22 0 17 Sep 2020
TripleTree: A Versatile Interpretable Representation of Black Box Agents and their Environments Tom Bewley J. Lawry FAtt 13 25 0 10 Sep 2020
$On $\ell_p$-norm Robustness of Ensemble Stumps and Trees$ On $\ell_p$ -norm Robustness of Ensemble Stumps and Trees Yihan Wang Huan Zhang Hongge Chen Duane S. Boning Cho-Jui Hsieh AAML 20 7 0 20 Aug 2020
MurTree: Optimal Classification Trees via Dynamic Programming and Search Emir Demirović Anna Lukina E. Hébrard Jeffrey Chan James Bailey C. Leckie K. Ramamohanarao Peter Stuckey 19 63 0 24 Jul 2020
Am I Building a White Box Agent or Interpreting a Black Box Agent? Tom Bewley 11 1 0 02 Jul 2020
IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis Hossein Hajipour Mateusz Malinowski Mario Fritz MIACV 9 3 0 18 Jun 2020
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts R. Akrour Davide Tateo Jan Peters 28 21 0 10 Jun 2020
PLANS: Robust Program Learning from Neurally Inferred Specifications Raphaël Dang-Nhu 22 2 0 05 Jun 2020
Formal Methods with a Touch of Magic P. A. Alamdari Guy Avni T. Henzinger Anna Lukina OffRL 12 16 0 25 May 2020
Automatic Discovery of Interpretable Planning Strategies Julian Skirzyñski Frederic Becker Falk Lieder 21 15 0 24 May 2020
Probabilistic Guarantees for Safe Deep Reinforcement Learning E. Bacci David Parker 19 27 0 14 May 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
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles Songyang Han Shangli Zhou Jiangwei Wang Lynn Pepin Caiwen Ding Jie Fu Fei Miao 34 22 0 09 Mar 2020
Learning by Cheating Dian Chen Brady Zhou V. Koltun Philipp Krahenbuhl SSL 47 504 0 27 Dec 2019
MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shielding Wenbo Zhang Osbert Bastani Vijay Kumar 14 39 0 25 Oct 2019
Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics Shuo Li Osbert Bastani 25 81 0 24 Oct 2019
Interpreting Deep Learning-Based Networking Systems Zili Meng Minhu Wang Jia-Ju Bai Mingwei Xu Hongzi Mao Hongxin Hu AI4CE 30 3 0 09 Oct 2019
Formal Language Constraints for Markov Decision Processes Eleanor Quint Dong Xu Haluk Dogan Stephen Scott Matthew B. Dwyer AI4CE 17 3 0 02 Oct 2019
Reinforcement Learning in Healthcare: A Survey Chao Yu Jiming Liu S. Nemati LM&MA OffRL 28 550 0 22 Aug 2019
A Symbolic Neural Network Representation and its Application to Understanding, Verifying, and Patching Networks Matthew Sotoudeh Aditya V. Thakur 6 4 0 17 Aug 2019
An Inductive Synthesis Framework for Verifiable Reinforcement Learning He Zhu Zikang Xiong Stephen Magill Suresh Jagannathan 33 93 0 16 Jul 2019