% Introduction
@article{dqn,
  title={Human-level control through deep reinforcement learning},
  author={Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A and Veness, Joel and Bellemare, Marc G and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K and Ostrovski, Georg and others},
  journal={nature},
  volume={518},
  number={7540},
  pages={529--533},
  year={2015},
  publisher={Nature Publishing Group}
}
@article{mnih2013playing,
  title={Playing atari with deep reinforcement learning},
  author={Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Graves, Alex and Antonoglou, Ioannis and Wierstra, Daan and Riedmiller, Martin},
  journal={arXiv preprint arXiv:1312.5602},
  year={2013}
}
@article{andrychowicz2020learning,
  title={Learning dexterous in-hand manipulation},
  author={Andrychowicz, OpenAI: Marcin and Baker, Bowen and Chociej, Maciek and Jozefowicz, Rafal and McGrew, Bob and Pachocki, Jakub and Petron, Arthur and Plappert, Matthias and Powell, Glenn and Ray, Alex and others},
  journal={The International Journal of Robotics Research},
  volume={39},
  number={1},
  pages={3--20},
  year={2020},
  publisher={SAGE Publications Sage UK: London, England}
}
@inproceedings{kalashnikov2018scalable,
  title={Scalable deep reinforcement learning for vision-based robotic manipulation},
  author={Kalashnikov, Dmitry and Irpan, Alex and Pastor, Peter and Ibarz, Julian and Herzog, Alexander and Jang, Eric and Quillen, Deirdre and Holly, Ethan and Kalakrishnan, Mrinal and Vanhoucke, Vincent and others},
  booktitle={Conference on Robot Learning},
  pages={651--673},
  year={2018},
  organization={PMLR}
}
@article{arel2010reinforcement,
  title={Reinforcement learning-based multi-agent system for network traffic signal control},
  author={Arel, Itamar and Liu, Cong and Urbanik, Tom and Kohls, Airton G},
  journal={IET Intelligent Transport Systems},
  volume={4},
  number={2},
  pages={128--135},
  year={2010},
  publisher={IET}
}
@article{zhou2017optimizing,
  title={Optimizing chemical reactions with deep reinforcement learning},
  author={Zhou, Zhenpeng and Li, Xiaocheng and Zare, Richard N},
  journal={ACS central science},
  volume={3},
  number={12},
  pages={1337--1344},
  year={2017},
  publisher={ACS Publications}
}
@article{huang2021deep,
  title={A deep reinforcement learning based long-term recommender system},
  author={Huang, Liwei and Fu, Mingsheng and Li, Fan and Qu, Hong and Liu, Yangjun and Chen, Wenyu},
  journal={Knowledge-Based Systems},
  volume={213},
  pages={106706},
  year={2021},
  publisher={Elsevier}
}

% ------------- past work --------------
% goal
@article{cruz2021explainable,
  title={Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario},
  author={Cruz, Francisco and Dazeley, Richard and Vamplew, Peter and Moreira, Ithan},
  journal={Neural Computing and Applications},
  pages={1--18},
  year={2021},
  publisher={Springer}
}
% reward
@inproceedings{juozapaitis2019explainable,
  title={Explainable reinforcement learning via reward decomposition},
  author={Juozapaitis, Zoe and Koul, Anurag and Fern, Alan and Erwig, Martin and Doshi-Velez, Finale},
  booktitle={IJCAI/ECAI Workshop on Explainable Artificial Intelligence},
  year={2019}
}
% history of interactions
@inproceedings{sequeira2019interestingness,
  title={Interestingness Elements for Explainable Reinforcement Learning through Introspection.},
  author={Sequeira, Pedro and Yeh, Eric and Gervasio, Melinda T},
  booktitle={IUI workshops},
  volume={1},
  year={2019}
}
@article{sequeira2020interestingness,
  title={Interestingness elements for explainable reinforcement learning: Understanding agents' capabilities and limitations},
  author={Sequeira, Pedro and Gervasio, Melinda},
  journal={Artificial Intelligence},
  volume={288},
  pages={103367},
  year={2020},
  publisher={Elsevier}
}
% Representation analysis
@article{lesort2018state,
  title={State representation learning for control: An overview},
  author={Lesort, Timoth{\'e}e and D{\'\i}az-Rodr{\'\i}guez, Natalia and Goudou, Jean-Franois and Filliat, David},
  journal={Neural Networks},
  volume={108},
  pages={379--392},
  year={2018},
  publisher={Elsevier}
}
@article{raffin2019decoupling,
  title={Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics},
  author={Raffin, Antonin and Hill, Ashley and Traor{\'e}, Ren{\'e} and Lesort, Timoth{\'e}e and D{\'\i}az-Rodr{\'\i}guez, Natalia and Filliat, David},
  journal={arXiv preprint arXiv:1901.08651},
  year={2019}
}
@article{raffin2018s,
  title={S-RL toolbox: Environments, datasets and evaluation metrics for state representation learning},
  author={Raffin, Antonin and Hill, Ashley and Traor{\'e}, Ren{\'e} and Lesort, Timoth{\'e}e and D{\'\i}az-Rodr{\'\i}guez, Natalia and Filliat, David},
  journal={arXiv preprint arXiv:1809.09369},
  year={2018}
}
@article{doncieux2020dream,
  title={DREAM architecture: a developmental approach to open-ended learning in robotics},
  author={Doncieux, Stephane and Bredeche, Nicolas and Goff, L{\'e}ni Le and Girard, Beno{\^\i}t and Coninx, Alexandre and Sigaud, Olivier and Khamassi, Mehdi and D{\'\i}az-Rodr{\'\i}guez, Natalia and Filliat, David and Hospedales, Timothy and others},
  journal={arXiv preprint arXiv:2005.06223},
  year={2020}
}
@article{doncieux2018open,
  title={Open-ended learning: a conceptual framework based on representational redescription},
  author={Doncieux, Stephane and Filliat, David and D{\'\i}az-Rodr{\'\i}guez, Natalia and Hospedales, Timothy and Duro, Richard and Coninx, Alexandre and Roijers, Diederik M and Girard, Beno{\^\i}t and Perrin, Nicolas and Sigaud, Olivier},
  journal={Frontiers in neurorobotics},
  volume={12},
  pages={59},
  year={2018},
  publisher={Frontiers Media SA}
}
@article{traore2019discorl,
  title={Discorl: Continual reinforcement learning via policy distillation},
  author={Traor{\'e}, Ren{\'e} and Caselles-Dupr{\'e}, Hugo and Lesort, Timoth{\'e}e and Sun, Te and Cai, Guanghang and D{\'\i}az-Rodr{\'\i}guez, Natalia and Filliat, David},
  journal={arXiv preprint arXiv:1907.05855},
  year={2019}
}


% Graph Explainability
@inproceedings{pope2019explainability,
  title={Explainability methods for graph convolutional neural networks},
  author={Pope, Phillip E and Kolouri, Soheil and Rostami, Mohammad and Martin, Charles E and Hoffmann, Heiko},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10772--10781},
  year={2019}
}
@article{baldassarre2019explainability,
  title={Explainability techniques for graph convolutional networks},
  author={Baldassarre, Federico and Azizpour, Hossein},
  journal={arXiv preprint arXiv:1905.13686},
  year={2019}
}

% saliency in Image and Graph Classification
@article{lrp,
  title={On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation},
  author={Bach, Sebastian and Binder, Alexander and Montavon, Gr{\'e}goire and Klauschen, Frederick and M{\"u}ller, Klaus-Robert and Samek, Wojciech},
  journal={PloS one},
  volume={10},
  number={7},
  pages={e0130140},
  year={2015},
  publisher={Public Library of Science San Francisco, CA USA}
}
@inproceedings{selvaraju2017grad,
  title={Grad-cam: Visual explanations from deep networks via gradient-based localization},
  author={Selvaraju, Ramprasaath R and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={618--626},
  year={2017}
}
@article{simonyan2013deep,
  title={Deep inside convolutional networks: Visualising image classification models and saliency maps},
  author={Simonyan, Karen and Vedaldi, Andrea and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1312.6034},
  year={2013}
}
@inproceedings{zhang2018interpretable,
  title={Interpretable convolutional neural networks},
  author={Zhang, Quanshi and Wu, Ying Nian and Zhu, Song-Chun},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={8827--8836},
  year={2018}
}
@inproceedings{zhou2016learning,
  title={Learning deep features for discriminative localization},
  author={Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Oliva, Aude and Torralba, Antonio},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2921--2929},
  year={2016}
}
@article{zhang2018top,
  title={Top-down neural attention by excitation backprop},
  author={Zhang, Jianming and Bargal, Sarah Adel and Lin, Zhe and Brandt, Jonathan and Shen, Xiaohui and Sclaroff, Stan},
  journal={International Journal of Computer Vision},
  volume={126},
  number={10},
  pages={1084--1102},
  year={2018},
  publisher={Springer}
}

% saliency in RL
@inproceedings{weitkamp2018visual,
  title={Visual rationalizations in deep reinforcement learning for atari games},
  author={Weitkamp, Laurens and van der Pol, Elise and Akata, Zeynep},
  booktitle={Benelux Conference on Artificial Intelligence},
  pages={151--165},
  year={2018},
  organization={Springer}
}
@inproceedings{greydanus2018visualizing,
  title={Visualizing and understanding atari agents},
  author={Greydanus, Samuel and Koul, Anurag and Dodge, Jonathan and Fern, Alan},
  booktitle={International Conference on Machine Learning},
  pages={1792--1801},
  year={2018},
  organization={PMLR}
}
@inproceedings{iyer2018transparency,
  title={Transparency and explanation in deep reinforcement learning neural networks},
  author={Iyer, Rahul and Li, Yuezhang and Li, Huao and Lewis, Michael and Sundar, Ramitha and Sycara, Katia},
  booktitle={Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
  pages={144--150},
  year={2018}
}
@inproceedings{huber2019enhancing,
  title={Enhancing explainability of deep reinforcement learning through selective layer-wise relevance propagation},
  author={Huber, Tobias and Schiller, Dominik and Andr{\'e}, Elisabeth},
  booktitle={Joint German/Austrian Conference on Artificial Intelligence (K{\"u}nstliche Intelligenz)},
  pages={188--202},
  year={2019},
  organization={Springer}
}

% graph networks in DRL robotics
@inproceedings{sanchez2018graph,
  title={Graph networks as learnable physics engines for inference and control},
  author={Sanchez-Gonzalez, Alvaro and Heess, Nicolas and Springenberg, Jost Tobias and Merel, Josh and Riedmiller, Martin and Hadsell, Raia and Battaglia, Peter},
  booktitle={International Conference on Machine Learning},
  pages={4470--4479},
  year={2018},
  organization={PMLR}
}

@inproceedings{wang2018nervenet,
  title={Nervenet: Learning structured policy with graph neural networks},
  author={Wang, Tingwu and Liao, Renjie and Ba, Jimmy and Fidler, Sanja},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

% data-driven machine maintenance
@article{zhang2019data,
  title={Data-driven methods for predictive maintenance of industrial equipment: A survey},
  author={Zhang, Weiting and Yang, Dong and Wang, Hongchao},
  journal={IEEE Systems Journal},
  volume={13},
  number={3},
  pages={2213--2227},
  year={2019},
  publisher={IEEE}
}

% mujoco 
@inproceedings{todorov2012mujoco,
  title={Mujoco: A physics engine for model-based control},
  author={Todorov, Emanuel and Erez, Tom and Tassa, Yuval},
  booktitle={2012 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  pages={5026--5033},
  year={2012},
  organization={IEEE}
}
@misc{openai-gym,
  title = {Getting Started with Gym},
  author={OpenAI},
  howpublished = {\url{https://gym.openai.com/docs/}},
  year=2021,
  note = {Accessed: 2022-01-29}
}
@misc{walker,
  title = {Walker2D},
  author={OpenAI},
  howpublished = {\url{https://www.gymlibrary.ml/environments/mujoco/walker2d/}},
  year=2022,
  note = {Accessed: 2022-08-16}
}

% ----------------- Graphs
@article{battaglia2018relational,
  title={Relational inductive biases, deep learning, and graph networks},
  author={Battaglia, Peter W and Hamrick, Jessica B and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and others},
  journal={arXiv preprint arXiv:1806.01261},
  year={2018}
}

% ------------------ SAC
@inproceedings{sac-v1,
  title={Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor},
  author={Haarnoja, Tuomas and Zhou, Aurick and Abbeel, Pieter and Levine, Sergey},
  booktitle={International conference on machine learning},
  pages={1861--1870},
  year={2018},
  organization={PMLR}
}
@article{sac-v2,
  title={Soft actor-critic algorithms and applications},
  author={Haarnoja, Tuomas and Zhou, Aurick and Hartikainen, Kristian and Tucker, George and Ha, Sehoon and Tan, Jie and Kumar, Vikash and Zhu, Henry and Gupta, Abhishek and Abbeel, Pieter and others},
  journal={arXiv preprint arXiv:1812.05905},
  year={2018}
}