Graying the black box: Understanding DQNs

8 February 2016

Papers citing "Graying the black box: Understanding DQNs"

50 / 66 papers shown

Title
Behaviour Discovery and Attribution for Explainable Reinforcement Learning Rishav Rishav Somjit Nath Vincent Michalski Samira Ebrahimi Kahou FAtt OffRL 73 0 0 19 Mar 2025
Policy-to-Language: Train LLMs to Explain Decisions with Flow-Matching Generated Rewards Xinyi Yang Liang Zeng Heng Dong Chao Yu X. Wu H. Yang Yu Wang Milind Tambe Tonghan Wang 83 2 0 18 Feb 2025
Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability Wen-Dong Jiang Chih-Yung Chang Show-Jane Yen Diptendu Sinha Roy FAtt HAI 90 1 0 02 Dec 2024
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
Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback Tom Bewley J. Lawry Arthur G. Richards 32 1 0 26 May 2023
AutoDOViz: Human-Centered Automation for Decision Optimization D. Weidele S. Afzal Abel N. Valente Cole Makuch Owen Cornec ... Radu Marinescu Paulito Palmes Elizabeth M. Daly Loraine Franke D. Haehn OffRL 50 4 0 19 Feb 2023
Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over Dropout Takuya Hiraoka Takashi Onishi Yoshimasa Tsuruoka OffRL 29 0 0 26 Jan 2023
Explainable Deep Reinforcement Learning: State of the Art and Challenges G. Vouros XAI 52 77 0 24 Jan 2023
Time-Efficient Reward Learning via Visually Assisted Cluster Ranking David Zhang Micah Carroll Andreea Bobu Anca Dragan 32 4 0 30 Nov 2022
Global and Local Analysis of Interestingness for Competency-Aware Deep Reinforcement Learning Pedro Sequeira Jesse Hostetler Melinda Gervasio 18 0 0 11 Nov 2022
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping Ronilo Ragodos Tong Wang Qihang Lin Xun Zhou 24 7 0 06 Nov 2022
Reward Learning with Trees: Methods and Evaluation Tom Bewley J. Lawry Arthur G. Richards R. Craddock Ian Henderson 31 1 0 03 Oct 2022
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees Zichuan Liu Zichuan Liu Zhi Wang Yuanyang Zhu Chunlin Chen 66 5 0 15 Sep 2022
Explainability in reinforcement learning: perspective and position Agneza Krajna Mario Brčič T. Lipić Juraj Dončević 39 27 0 22 Mar 2022
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act Alexis Jacq Johan Ferret Olivier Pietquin M. Geist 32 9 0 16 Mar 2022
A Survey of Explainable Reinforcement Learning Stephanie Milani Nicholay Topin Manuela Veloso Fei Fang XAI LRM 35 52 0 17 Feb 2022
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning Wenjie Shi Gao Huang Shiji Song Cheng Wu 34 9 0 06 Dec 2021
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey Richard Dazeley Peter Vamplew Francisco Cruz 32 60 0 20 Aug 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
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 53 170 0 13 Jan 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 35 63 0 18 Dec 2020
Demystifying Deep Neural Networks Through Interpretation: A Survey Giang Dao Minwoo Lee FaML FAtt 22 1 0 13 Dec 2020
Meta-trained agents implement Bayes-optimal agents Vladimir Mikulik Grégoire Delétang Tom McGrath Tim Genewein Miljan Martic Shane Legg Pedro A. Ortega OOD FedML 37 41 0 21 Oct 2020
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research Cody Watson Nathan Cooper David Nader-Palacio Kevin Moran Denys Poshyvanyk 26 111 0 14 Sep 2020
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps Tobias Huber Katharina Weitz Elisabeth André Ofra Amir FAtt 21 64 0 18 May 2020
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition Mahsan Nourani Chiradeep Roy Tahrima Rahman Eric D. Ragan Nicholas Ruozzi Vibhav Gogate AAML 15 17 0 05 May 2020
How Do You Act? An Empirical Study to Understand Behavior of Deep Reinforcement Learning Agents Richard Meyes Moritz Schneider Tobias Meisen 28 2 0 07 Apr 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 51 82 0 17 Mar 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
The Emerging Landscape of Explainable AI Planning and Decision Making Tathagata Chakraborti S. Sreedharan S. Kambhampati 35 112 0 26 Feb 2020
How Transferable are the Representations Learned by Deep Q Agents? Jacob Tyo Zachary Chase Lipton OffRL 19 6 0 24 Feb 2020
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution Nikaash Puri Sukriti Verma Piyush B. Gupta Dhruv Kayastha Shripad Deshmukh Balaji Krishnamurthy Sameer Singh FAtt AAML 19 75 0 23 Dec 2019
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and Limitations Pedro Sequeira Melinda Gervasio 19 104 0 19 Dec 2019
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning Akanksha Atrey Kaleigh Clary David D. Jensen FAtt LRM 30 90 0 09 Dec 2019
Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning Haodi Zhang Zihang Gao Yi Zhou Haotong Zhang Kaishun Wu Fangzhen Lin AI4CE 27 17 0 22 Oct 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
DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning Theo Jaunet Romain Vuillemot Christian Wolf HAI 18 36 0 06 Sep 2019
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents Alex Mott Daniel Zoran Mike Chrzanowski Daan Wierstra Danilo Jimenez Rezende 26 188 0 06 Jun 2019
Generation of Policy-Level Explanations for Reinforcement Learning Nicholay Topin Manuela Veloso 32 74 0 28 May 2019
Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review Benyamin Ghojogh Maria N. Samad Sayema Asif Mashhadi Tania Kapoor Wahab Ali Fakhri Karray Mark Crowley 26 89 0 07 May 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 17 996 0 26 Feb 2019
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning Qianwen Wang Yao Ming Zhihua Jin Qiaomu Shen Dongyu Liu Micah J. Smith K. Veeramachaneni Huamin Qu HAI 22 101 0 13 Feb 2019
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching Tom Zahavy Shie Mannor HAI 36 30 0 24 Jan 2019
Dopamine: A Research Framework for Deep Reinforcement Learning Pablo Samuel Castro Subhodeep Moitra Carles Gelada Saurabh Kumar Marc G. Bellemare OffRL 28 276 0 14 Dec 2018
Learning Finite State Representations of Recurrent Policy Networks Anurag Koul S. Greydanus Alan Fern 19 88 0 29 Nov 2018
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems Sina Mohseni Niloofar Zarei Eric D. Ragan 38 102 0 28 Nov 2018
Scalable agent alignment via reward modeling: a research direction Jan Leike David M. Krueger Tom Everitt Miljan Martic Vishal Maini Shane Legg 34 397 0 19 Nov 2018
An initial attempt of combining visual selective attention with deep reinforcement learning Liu Yuezhang Ruohan Zhang D. Ballard 32 20 0 11 Nov 2018
A Survey and Critique of Multiagent Deep Reinforcement Learning Pablo Hernandez-Leal Bilal Kartal Matthew E. Taylor OffRL 48 553 0 12 Oct 2018