Programmatically Interpretable Reinforcement Learning

6 April 2018

Swarat Chaudhuri

Papers citing "Programmatically Interpretable Reinforcement Learning"

22 / 72 papers shown

Title
Deep Interpretable Models of Theory of Mind Ini Oguntola Dana Hughes Katia Sycara HAI 33 26 0 07 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
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
Neurogenetic Programming Framework for Explainable Reinforcement Learning Vadim Liventsev Aki Härmä M. Petković 18 4 0 08 Feb 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
What Did You Think Would Happen? Explaining Agent Behaviour Through Intended Outcomes Herman Yau Chris Russell Simon Hadfield FAtt LRM 28 36 0 10 Nov 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
Learning Differentiable Programs with Admissible Neural Heuristics Ameesh Shah Eric Zhan Jennifer J. Sun Abhinav Verma Yisong Yue Swarat Chaudhuri 149 43 0 23 Jul 2020
Automatic Discovery of Interpretable Planning Strategies Julian Skirzyñski Frederic Becker Falk Lieder 21 15 0 24 May 2020
NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems Hoang-Dung Tran Xiaodong Yang Diego Manzanas Lopez Patrick Musau L. V. Nguyen Weiming Xiang Stanley Bak Taylor T. Johnson 34 239 0 12 Apr 2020
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning Akanksha Atrey Kaleigh Clary David D. Jensen FAtt LRM 24 90 0 09 Dec 2019
Counterfactual States for Atari Agents via Generative Deep Learning Matthew Lyle Olson Lawrence Neal Fuxin Li Weng-Keen Wong CML 21 29 0 27 Sep 2019
Learning Compositional Neural Programs with Recursive Tree Search and Planning Thomas Pierrot Guillaume Ligner Scott E. Reed Olivier Sigaud Nicolas Perrin Alexandre Laterre David Kas Karim Beguir Nando de Freitas 41 41 0 30 May 2019
Few-Shot Bayesian Imitation Learning with Logical Program Policies Tom Silver Kelsey R. Allen Alexander K. Lew L. Kaelbling J. Tenenbaum LM&Ro 26 50 0 12 Apr 2019
From explanation to synthesis: Compositional program induction for learning from demonstration Michael G. Burke Svetlin Penkov S. Ramamoorthy 25 20 0 27 Feb 2019
Agent Embeddings: A Latent Representation for Pole-Balancing Networks Oscar Chang Robert Kwiatkowski Siyuan Chen Hod Lipson 27 6 0 12 Nov 2018
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration Ritesh Noothigattu Djallel Bouneffouf Nicholas Mattei Rachita Chandra Piyush Madan Kush R. Varshney Murray Campbell Moninder Singh F. Rossi AI4CE 16 23 0 21 Sep 2018
Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences J. V. D. Waa J. Diggelen K. Bosch Mark Antonius Neerincx OffRL 31 107 0 23 Jul 2018
Verifiable Reinforcement Learning via Policy Extraction Osbert Bastani Yewen Pu Armando Solar-Lezama OffRL 26 329 0 22 May 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 251 1,842 0 03 Feb 2017