Fetching the paper…
Reading the bibliography…
The black-box nature of deep reinforcement learning (RL) hinders them from real-world applications.
T. Zahavy, N. Ben-Zrihem, and S. Mannor, “Graying the black box: Understanding dqns,” in
1908
Earlier work this paper cites.
R. S. Sutton, A. G. Barto
1998
Earlier work this paper cites.
N. Tishby, F. C. N. Pereira, and W. Bialek, “The information bottleneck method,”
2000
Earlier work this paper cites.
Y. Engel and S. Mannor, “Learning embedded maps of markov processes,” in
2001
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.”
2008
Earlier work this paper cites.
J. Pearl,
2009
Earlier work this paper cites.
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling, “The arcade learning environment: An evaluation platform for general agents,”
2013
Earlier work this paper cites.
K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep inside convolutional networks: Visualising image classification models and saliency maps,” in
2014
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski
2015
Earlier work this paper cites.
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek, “On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,”
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox, “A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,” in
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
J. Choi, B.-J. Lee, and B.-T. Zhang, “Multi-focus attention network for efficient deep reinforcement learning,” in
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in
2017
Earlier work this paper cites.
R. C. Fong and A. Vedaldi, “Interpretable explanations of black boxes by meaningful perturbation,” in
2017
Earlier work this paper cites.
P. Dabkowski and Y. Gal, “Real time image saliency for black box classifiers,” in
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Adadi and M. Berrada, “Peeking inside the black-box: A survey on explainable artificial intelligence (XAI),”
2018
Earlier work this paper cites.
F. K. Dosilovic, M. Brcic, and N. Hlupic, “Explainable artificial intelligence: A survey,” in
2018
Earlier work this paper cites.
S. Greydanus, A. Koul, J. Dodge, and A. Fern, “Visualizing and understanding atari agents,” in
2018
Cited alongside, same era.
V. Petsiuk, A. Das, and K. Saenko, “RISE: randomized input sampling for explanation of black-box models,” in
2018
Cited alongside, same era.
M. Edmonds, J. Kubricht, C. Summers, Y. Zhu, B. Rothrock, S.-C. Zhu, and H. Lu, “Human causal transfer: Challenges for deep reinforcement learning.” in
2018
Cited alongside, same era.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in
2018
Cited alongside, same era.
M. Chevalier-Boisvert, F. Golemo, Y. Cao, B. Mehta, and L. Paull, “Duckietown environments for openai gym,”
2018
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
R. Guo, L. Cheng, J. Li, P. R. Hahn, and H. Liu, “A survey of learning causality with data: Problems and methods,”
2020
Later among the works it cites.
A. Kumar, A. Zhou, G. Tucker, and S. Levine, “Conservative q-learning for offline reinforcement learning,” in
2020
Later among the works it cites.
A. Chatzimparmpas, R. M. Martins, I. Jusufi, and A. Kerren, “A survey of surveys on the use of visualization for interpreting machine learning models,”
2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev
2019
Cited alongside, same era.
2019
Cited alongside, same era.
R. M. Annasamy and K. Sycara, “Towards better interpretability in deep q-networks,” in
2019
Cited alongside, same era.
A. Manchin, E. Abbasnejad, and A. van den Hengel, “Reinforcement learning with attention that works: A self-supervised approach,” in
2019
Cited alongside, same era.
D. Nikulin, A. Ianina, V. Aliev, and S. I. Nikolenko, “Free-lunch saliency via attention in atari agents,” in
2019
Cited alongside, same era.
A. Mott, D. Zoran, M. Chrzanowski, D. Wierstra, and D. J. Rezende, “Towards interpretable reinforcement learning using attention augmented agents,” in
2019
Cited alongside, same era.
R. Fong, M. Patrick, and A. Vedaldi, “Understanding deep networks via extremal perturbations and smooth masks,” in
2019
Cited alongside, same era.
Later among the works it cites.
A. Alharin, T.-N. Doan, and M. Sartipi, “Reinforcement learning interpretation methods: A survey,”
2020
Later among the works it cites.
C. Glanois, P. Weng, M. Zimmer, D. Li, T. Yang, J. Hao, and W. Liu, “A survey on interpretable reinforcement learning,”
2021
Later among the works it cites.
A. Heuillet, F. Couthouis, and N. Díaz-Rodríguez, “Explainability in deep reinforcement learning,”
2021
Later among the works it cites.
Q. Zhang, X. Ma, Y. Yang, C. Li, J. Yang, Y. Liu, and B. Liang, “Learning to discover task-relevant features for interpretable reinforcement learning,”
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Kim and M. Bansal, “Towards an interpretable deep driving network by attentional bottleneck,”
2021
Later among the works it cites.
V. Kim, H. Cho, and S. Chung, “One-step pixel-level perturbation-based saliency detector,” 2021
2021
Later among the works it cites.
P. S. Parvatharaju, R. Doddaiah, T. Hartvigsen, and E. A. Rundensteiner, “Learning saliency maps to explain deep time series classifiers,” in
2021
Later among the works it cites.
M. Ivanovs, R. Kadikis, and K. Ozols, “Perturbation-based methods for explaining deep neural networks: A survey,”
2021
Later among the works it cites.
W. Shi, G. Huang, S. Song, and C. Wu, “Temporal-spatial causal interpretations for vision-based reinforcement learning,”
2021
Later among the works it cites.
H. Wu, K. Khetarpal, and D. Precup, “Self-supervised attention-aware reinforcement learning,” in
2021
Later among the works it cites.
G. Liu, X. Sun, O. Schulte, and P. Poupart, “Learning tree interpretation from object representation for deep reinforcement learning,” in
2021
Later among the works it cites.
L. He, N. Aouf, and B. Song, “Explainable deep reinforcement learning for uav autonomous path planning,”
2021
Later among the works it cites.
T. Han, S. Nageshrao, D. P. Filev, K. A. Redmill, and Ü. Özgüner, “An online evolving method for a safe and fast automated vehicle control system,”
2022
Later among the works it cites.
2022
Later among the works it cites.
I. Kostrikov, A. Nair, and S. Levine, “Offline reinforcement learning with implicit q-learning,” in
2022
Later among the works it cites.
2022
Later among the works it cites.
A. Zytek, I. Arnaldo, D. Liu, L. Berti-Équille, and K. Veeramachaneni, “The need for interpretable features: Motivation and taxonomy,”
2022
Later among the works it cites.