Towards Better Interpretability in Deep Q-Networks

15 September 2018

Papers citing "Towards Better Interpretability in Deep Q-Networks"

15 / 15 papers shown

Title
Leveraging Reward Consistency for Interpretable Feature Discovery in Reinforcement Learning Qisen Yang Huanqian Wang Mukun Tong Wenjie Shi Gao Huang Shiji Song 40 5 0 04 Sep 2023
A Closer Look at Reward Decomposition for High-Level Robotic Explanations Wenhao Lu Xufeng Zhao S. Magg M. Gromniak Mengdi Li S. Wermter 42 7 0 25 Apr 2023
Explainable Deep Reinforcement Learning: State of the Art and Challenges G. Vouros XAI 55 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
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping Ronilo Ragodos Tong Wang Qihang Lin Xun Zhou 24 7 0 06 Nov 2022
Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders Per-Arne Andersen Ole-Christoffer Granmo Morten Goodwin OOD 36 0 0 03 Oct 2022
Contrastive Learning as Goal-Conditioned Reinforcement Learning Benjamin Eysenbach Tianjun Zhang Ruslan Salakhutdinov Sergey Levine SSL OffRL 42 141 0 15 Jun 2022
A Survey of Explainable Reinforcement Learning Stephanie Milani Nicholay Topin Manuela Veloso Fei Fang XAI LRM 35 53 0 17 Feb 2022
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning Wenjie Shi Gao Huang Shiji Song Cheng Wu 36 9 0 06 Dec 2021
Deep Interpretable Models of Theory of Mind Ini Oguntola Dana Hughes Katia Sycara HAI 33 26 0 07 Apr 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
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
Automatic Discovery of Interpretable Planning Strategies Julian Skirzyñski Frederic Becker Falk Lieder 21 15 0 24 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
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