@article{cardoso2003dependence,
  title={Dependence, correlation and gaussianity in independent component analysis},
  author={Cardoso, Jean-Fran{\c{c}}ois},
  journal={Journal of Machine Learning Research},
  volume={4},
  number={Dec},
  pages={1177--1203},
  year={2003}
}
@article{ver2014discovering,
  title={Discovering structure in high-dimensional data through correlation explanation},
  author={Ver Steeg, Greg and Galstyan, Aram},
  journal={Advances in Neural Information Processing Systems},
  volume={27},
  year={2014}
}
@inproceedings{chen2018isolating,
  title={Isolating sources of disentanglement in variational autoencoders},
  author={Chen, Tian Qi and Li, Xuechen and Grosse, Roger B and Duvenaud, David K},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2610--2620},
  year={2018}
}
@inproceedings{locatello2019fairness,
  title={On the fairness of disentangled representations},
  author={Locatello, Francesco and Abbati, Gabriele and Rainforth, Thomas and Bauer, Stefan and Sch{\"o}lkopf, Bernhard and Bachem, Olivier},
  booktitle={Advances in Neural Information Processing Systems},
  pages={14611--14624},
  year={2019}
}
@inproceedings{kim2018disentangling,
  title={Disentangling by Factorising},
  author={Kim, Hyunjik and Mnih, Andriy},
  booktitle={International Conference on Machine Learning},
  pages={2649--2658},
  year={2018}
}





@inproceedings{mvrl_ssm,
author = {Hwang, HyeongJoo and Seo, Seokin and Jang, Youngsoo and Kim, Sungyoon and Kim, Geon-Hyeong and Hong, Seunghoon and Kim, Kee-Eung},
title = {Information-Theoretic State Space Model for Multi-View Reinforcement Learning},
year = {2023},
publisher = {JMLR.org},
abstract = {Multi-View Reinforcement Learning (MVRL) seeks to find an optimal control for an agent given multi-view observations from various sources. Despite recent advances in multi-view learning that aim to extract the latent representation from multi-view data, it is not straightforward to apply them to control tasks, especially when the observations are temporally dependent on one another. The problem can be even more challenging if the observations are intermittently missing for a subset of views. In this paper, we introduce Fuse2Control (F2C), an information-theoretic approach to capturing the underlying state space model from the sequences of multi-view observations. We conduct an extensive set of experiments in various control tasks showing that our method is highly effective in aggregating task-relevant information across many views, that scales linearly with the number of views while retaining robustness to arbitrary missing view scenarios.},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
articleno = {581},
numpages = {34},
location = {Honolulu, Hawaii, USA},
series = {ICML'23}
}

@misc{cetin2022stabilizing,
      title={Stabilizing Off-Policy Deep Reinforcement Learning from Pixels}, 
      author={Edoardo Cetin and Philip J. Ball and Steve Roberts and Oya Celiktutan},
      year={2022},
      eprint={2207.00986},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{da_methods,
      title={Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning}, 
      author={Guozheng Ma and Linrui Zhang and Haoyu Wang and Lu Li and Zilin Wang and Zhen Wang and Li Shen and Xueqian Wang and Dacheng Tao},
      year={2023},
      eprint={2305.16379},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{submodMI1,
author = {Krause, Andreas and Singh, Ajit and Guestrin, Carlos},
title = {Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies},
year = {2008},
issue_date = {6/1/2008},
publisher = {JMLR.org},
volume = {9},
issn = {1532-4435},
abstract = {When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to address this task, for example, geometry or disk models, placing sensors at the points of highest entropy (variance) in the GP model, and A-, D-, or E-optimal design. In this paper, we tackle the combinatorial optimization problem of maximizing the mutual information between the chosen locations and the locations which are not selected. We prove that the problem of finding the configuration that maximizes mutual information is NP-complete. To address this issue, we describe a polynomial-time approximation that is within (1-1/e) of the optimum by exploiting the submodularity of mutual information. We also show how submodularity can be used to obtain online bounds, and design branch and bound search procedures. We then extend our algorithm to exploit lazy evaluations and local structure in the GP, yielding significant speedups. We also extend our approach to find placements which are robust against node failures and uncertainties in the model. These extensions are again associated with rigorous theoretical approximation guarantees, exploiting the submodularity of the objective function. We demonstrate the advantages of our approach towards optimizing mutual information in a very extensive empirical study on two real-world data sets.},
journal = {J. Mach. Learn. Res.},
month = {jun},
pages = {235–284},
numpages = {50}
}

@Article{submodMI2,
author={Nemhauser, G. L.
and Wolsey, L. A.
and Fisher, M. L.},
title={An analysis of approximations for maximizing submodular set functions---I},
journal={Mathematical Programming},
year={1978},
month={Dec},
day={01},
volume={14},
number={1},
pages={265-294},
abstract={LetN be a finite set andz be a real-valued function defined on the set of subsets ofN that satisfies z(S)+z(T)≥z(S⋃T)+z(S⋂T) for allS, T inN. Such a function is called submodular. We consider the problem maxS⊂N{\{}a(S):|S|≤K,z(S) submodular{\}}.},
issn={1436-4646},
doi={10.1007/BF01588971},
url={https://doi.org/10.1007/BF01588971}
}




@misc{shang2023active,
      title={Active Reinforcement Learning under Limited Visual Observability}, 
      author={Jinghuan Shang and Michael S. Ryoo},
      year={2023},
      eprint={2306.00975},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{Hansen2021StabilizingDQ,
  title={Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation},
  author={Nicklas Hansen and Hao Su and Xiaolong Wang},
  booktitle={Neural Information Processing Systems},
  year={2021},
  url={https://api.semanticscholar.org/CorpusID:235694513}
}

@article{Hsu2022VisionBasedMN,
  title={Vision-Based Manipulators Need to Also See from Their Hands},
  author={Kyle Hsu and Moo Jin Kim and Rafael Rafailov and Jiajun Wu and Chelsea Finn},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.12677},
  url={https://api.semanticscholar.org/CorpusID:247628166}
}

@misc{hansen2021generalization,
      title={Generalization in Reinforcement Learning by Soft Data Augmentation}, 
      author={Nicklas Hansen and Xiaolong Wang},
      year={2021},
      eprint={2011.13389},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}



# ==========================================================================
@inproceedings{infomax,
 author = {Mazoure, Bogdan and Tachet des Combes, Remi and Doan, Thang Long and Bachman, Philip and Hjelm, R Devon},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {3686--3698},
 publisher = {Curran Associates, Inc.},
 title = {Deep Reinforcement and InfoMax Learning},
 url = {https://proceedings.neurips.cc/paper/2020/file/26588e932c7ccfa1df309280702fe1b5-Paper.pdf},
 volume = {33},
 year = {2020}
}



@article{survey_mvrl,
  author    = {Yingming Li and
               Ming Yang and
               Zhongfei Zhang},
  title     = {Multi-View Representation Learning: {A} Survey from Shallow Methods
               to Deep Methods},
  journal   = {CoRR},
  volume    = {abs/1610.01206},
  year      = {2016},
  url       = {http://arxiv.org/abs/1610.01206},
  eprinttype = {arXiv},
  eprint    = {1610.01206},
  timestamp = {Thu, 08 Oct 2020 17:37:28 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/LiYZ16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{spatial_atten,
  author    = {Sergey Zagoruyko and
               Nikos Komodakis},
  title     = {Paying More Attention to Attention: Improving the Performance of Convolutional
               Neural Networks via Attention Transfer},
  booktitle = {5th International Conference on Learning Representations, {ICLR} 2017,
               Toulon, France, April 24-26, 2017, Conference Track Proceedings},
  publisher = {OpenReview.net},
  year      = {2017},
  url       = {https://openreview.net/forum?id=Sks9\_ajex},
  timestamp = {Thu, 25 Jul 2019 14:25:41 +0200},
  biburl    = {https://dblp.org/rec/conf/iclr/ZagoruykoK17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{rep_rl_survey1,
  author    = {Robert Kirk and
               Amy Zhang and
               Edward Grefenstette and
               Tim Rockt{\"{a}}schel},
  title     = {A Survey of Generalisation in Deep Reinforcement Learning},
  journal   = {CoRR},
  volume    = {abs/2111.09794},
  year      = {2021},
  url       = {https://arxiv.org/abs/2111.09794},
  eprinttype = {arXiv},
  eprint    = {2111.09794},
  timestamp = {Mon, 22 Nov 2021 16:44:06 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2111-09794.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{entro_reg2,
author = {Ziebart, Brian D. and Maas, Andrew and Bagnell, J. Andrew and Dey, Anind K.},
title = {Maximum Entropy Inverse Reinforcement Learning},
year = {2008},
isbn = {9781577353683},
publisher = {AAAI Press},
abstract = {Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recovering a utility function that makes the behavior induced by a near-optimal policy closely mimic demonstrated behavior. In this work, we develop a probabilistic approach based on the principle of maximum entropy. Our approach provides a well-defined, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods.We develop our technique in the context of modeling real-world navigation and driving behaviors where collected data is inherently noisy and imperfect. Our probabilistic approach enables modeling of route preferences as well as a powerful new approach to inferring destinations and routes based on partial trajectories.},
booktitle = {Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 3},
pages = {1433–1438},
numpages = {6},
location = {Chicago, Illinois},
series = {AAAI'08}
}

@inproceedings{entro_reg1,
author = {Haarnoja, Tuomas and Tang, Haoran and Abbeel, Pieter and Levine, Sergey},
title = {Reinforcement Learning with Deep Energy-Based Policies},
year = {2017},
publisher = {JMLR.org},
abstract = {We propose a method for learning expressive energy-based policies for continuous states and actions, which has been feasible only in tabular domains before. We apply our method to learning maximum entropy policies, resulting into a new algorithm, called soft Q-learning, that expresses the optimal policy via a Boltzmann distribution. We use the recently proposed amortized Stein variational gradient descent to learn a stochastic sampling network that approximates samples from this distribution. The benefits of the proposed algorithm include improved exploration and compositionality that allows transferring skills between tasks, which we confirm in simulated experiments with swimming and walking robots. We also draw a connection to actor-critic methods, which can be viewed performing approximate inference on the corresponding energy-based model.},
booktitle = {Proceedings of the 34th International Conference on Machine Learning - Volume 70},
pages = {1352–1361},
numpages = {10},
location = {Sydney, NSW, Australia},
series = {ICML'17}
}

@InProceedings{sapien,
author = {Xiang, Fanbo and Qin, Yuzhe and Mo, Kaichun and Xia, Yikuan and Zhu, Hao and Liu, Fangchen and Liu, Minghua and Jiang, Hanxiao and Yuan, Yifu and Wang, He and Yi, Li and Chang, Angel X. and Guibas, Leonidas J. and Su, Hao},
title = {{SAPIEN}: A SimulAted Part-based Interactive ENvironment},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}}

@inproceedings{DMVRL,
  author    = {Wang, Weiran and Arora, Raman and Livescu, Karen and Bilmes, Jeff},
  title     = {On Deep Multi-View Representation Learning},
  year      = {2015},
  publisher = {JMLR.org},
  abstract  = {We consider learning representations (features) in the setting in which we have access to multiple unlabeled views of the data for representation learning while only one view is available at test time. Previous work on this problem has proposed several techniques based on deep neural networks, typically involving either autoencoder-like networks with a reconstruction objective or paired feedforward networks with a correlation-based objective. We analyze several techniques based on prior work, as well as new variants, and compare them experimentally on visual, speech, and language domains. To our knowledge this is the first head-to-head comparison of a variety of such techniques on multiple tasks. We find an advantage for correlation-based representation learning, while the best results on most tasks are obtained with our new variant, deep canonically correlated autoencoders (DCCAE).},
  booktitle = {Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37},
  pages     = {1083–1092},
  numpages  = {10},
  location  = {Lille, France},
  series    = {ICML'15}
}

@article{DSpectralRL,
  author  = {Huang, Zhenyu and Zhou, Joey Tianyi and Zhu, Hongyuan and Zhang, Changqing and Lv, Jiancheng and Peng, Xi},
  journal = {IEEE Transactions on Image Processing},
  title   = {Deep Spectral Representation Learning From Multi-View Data},
  year    = {2021},
  volume  = {30},
  number  = {},
  pages   = {5352-5362},
  doi     = {10.1109/TIP.2021.3083072}
}

@inproceedings{FlexibleMVRL,
  title     = {Flexible Multi-View Representation Learning for Subspace Clustering},
  author    = {Li, Ruihuang and Zhang, Changqing and Hu, Qinghua and Zhu, Pengfei and Wang, Zheng},
  booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on
               Artificial Intelligence, {IJCAI-19}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  pages     = {2916--2922},
  year      = {2019},
  month     = {7},
  doi       = {10.24963/ijcai.2019/404},
  url       = {https://doi.org/10.24963/ijcai.2019/404}
}

@inproceedings{MFN,
  author    = {Zadeh, Amir and Liang, Paul Pu and Mazumder, Navonil and Poria, Soujanya and Cambria, Erik and Morency, Louis-Philippe},
  title     = {Memory Fusion Network for Multi-View Sequential Learning},
  year      = {2018},
  isbn      = {978-1-57735-800-8},
  publisher = {AAAI Press},
  abstract  = {Multi-view sequential learning is a fundamental problem in machine learning dealing with multi-view sequences. In a multi-view sequence, there exists two forms of interactions between different views: view-specific interactions and cross-view interactions. In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time. The first component of the MFN is called the System of LSTMs, where view-specific interactions are learned in isolation through assigning an LSTM function to each view. The cross-view interactions are then identified using a special attention mechanism called the Delta-memory Attention Network (DMAN) and summarized through time with a Multi-view Gated Memory. Through extensive experimentation, MFN is compared to various proposed approaches for multi-view sequential learning on multiple publicly available benchmark datasets. MFN outperforms all the multi-view approaches. Furthermore, MFN outperforms all current state-of-the-art models, setting new state-of-the-art results for all three multi-view datasets.},
  booktitle = {Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence},
  articleno = {691},
  numpages  = {8},
  location  = {New Orleans, Louisiana, USA},
  series    = {AAAI'18/IAAI'18/EAAI'18}
}

@inproceedings{MuMMI,
  author    = {Chen, Kaiqi and Lee, Yong and Soh, Harold},
  title     = {Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning},
  year      = {2021},
  publisher = {IEEE Press},
  url       = {https://doi.org/10.1109/ICRA48506.2021.9561187},
  doi       = {10.1109/ICRA48506.2021.9561187},
  abstract  = {This work focuses on learning useful and robust deep world models using multiple, possibly unreliable, sensors. We find that current methods do not sufficiently encourage a shared representation between modalities; this can cause poor performance on downstream tasks and over-reliance on specific sensors. As a solution, we contribute a new multi-modal deep latent state-space model, trained using a mutual information lower-bound. The key innovation is a specially-designed density ratio estimator that encourages consistency between the latent codes of each modality. We tasked our method to learn policies (in a self-supervised manner) on multi-modal Natural MuJoCo benchmarks and a challenging Table Wiping task. Experiments show our method significantly outperforms state-of-the-art deep reinforcement learning methods, particularly in the presence of missing observations.},
  booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
  pages     = {4274–4280},
  numpages  = {7},
  location  = {Xi'an, China}
}

﻿@article{Chen2022,
  author   = {Chen, Man-Sheng
              and Lin, Jia-Qi
              and Li, Xiang-Long
              and Liu, Bao-Yu
              and Wang, Chang-Dong
              and Huang, Dong
              and Lai, Jian-Huang},
  title    = {Representation Learning in Multi-view Clustering: A Literature Review},
  journal  = {Data Science and Engineering},
  year     = {2022},
  month    = {Sep},
  day      = {01},
  volume   = {7},
  number   = {3},
  pages    = {225-241},
  abstract = {Multi-view clustering (MVC) has attracted more and more attention in the recent few years by making full use of complementary and consensus information between multiple views to cluster objects into different partitions. Although there have been two existing works for MVC survey, neither of them jointly takes the recent popular deep learning-based methods into consideration. Therefore, in this paper, we conduct a comprehensive survey of MVC from the perspective of representation learning. It covers a quantity of multi-view clustering methods including the deep learning-based models, providing a novel taxonomy of the MVC algorithms. Furthermore, the representation learning-based MVC methods can be mainly divided into two categories, i.e., shallow representation learning-based MVC and deep representation learning-based MVC, where the deep learning-based models are capable of handling more complex data structure as well as showing better expression. In the shallow category, according to the means of representation learning, we further split it into two groups, i.e., multi-view graph clustering and multi-view subspace clustering. To be more comprehensive, basic research materials of MVC are provided for readers, containing introductions of the commonly used multi-view datasets with the download link and the open source code library. In the end, some open problems are pointed out for further investigation and development.},
  issn     = {2364-1541},
  doi      = {10.1007/s41019-022-00190-8},
  url      = {https://doi.org/10.1007/s41019-022-00190-8}
}

@article{UncertaintyMVRL,
  title        = {Uncertainty-Aware Multi-View Representation Learning},
  volume       = {35},
  url          = {https://ojs.aaai.org/index.php/AAAI/article/view/16924},
  doi          = {10.1609/aaai.v35i9.16924},
  abstractnote = {Learning from different data views by exploring the underlying complementary information among them can endow the representation with stronger expressive ability. However, high-dimensional features tend to contain noise, and furthermore, quality of data usually varies for different samples (even for different views), i.e., one view may be informative for one sample but not the case for another. Therefore, it is quite challenging to integrate multi-view noisy data under unsupervised setting. Traditional multi-view methods either simply treat each view with equal importance or tune the weights of different views to fixed values, which are insufficient to capture the dynamic noise in multi-view data. In this work, we devise a novel unsupervised multi-view learning approach, termed as Dynamic Uncertainty-Aware Networks (DUA-Nets). Guided by the uncertainty of data estimated from the generation perspective, intrinsic information from multiple views is integrated to obtain noise-free representations. Under the help of uncertainty estimation, DUA-Nets weigh each view of individual sample according to data quality so that the high-quality samples (or views) can be fully exploited while the effects from the noisy samples (or views) will be alleviated. Our model achieves superior performance in extensive experiments and shows the robustness to noisy data.},
  number       = {9},
  journal      = {Proceedings of the AAAI Conference on Artificial Intelligence},
  author       = {Geng, Yu and Han, Zongbo and Zhang, Changqing and Hu, Qinghua},
  year         = {2021},
  month        = {May},
  pages        = {7545-7553}
}


@inproceedings{CPMNet,
 author = {Zhang, Changqing and Han, Zongbo and cui, yajie and Fu, Huazhu and Zhou, Joey Tianyi and Hu, Qinghua},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {CPM-Nets: Cross Partial Multi-View Networks},
 url = {https://proceedings.neurips.cc/paper/2019/file/11b9842e0a271ff252c1903e7132cd68-Paper.pdf},
 volume = {32},
 year = {2019}
}




@InProceedings{S2R,
  title = 	 {Self-supervised representations for multi-view reinforcement learning},
  author =       {Yang, Huanhuan and Shi, Dianxi and Xie, Guojun and Peng, Yingxuan and Zhang, Yi and Yang, Yantai and Yang, Shaowu},
  booktitle = 	 {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {2203--2213},
  year = 	 {2022},
  editor = 	 {Cussens, James and Zhang, Kun},
  volume = 	 {180},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {01--05 Aug},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v180/yang22b/yang22b.pdf},
  url = 	 {https://proceedings.mlr.press/v180/yang22b.html},
  abstract = 	 {Learning policies from raw, pixel images are quite important for the real-world application of deep reinforcement learning (RL). Standard model-free RL algorithms focus on single-view settings and unify the representation learning and policy learning into an end-to-end training process. However, such a learning paradigm is sample-inefficiency and sensitive to hyper-parameters when supervised merely by the reward signals. Based on this, we present Self-Supervised Representations (S2R) for multi-view reinforcement learning, a sample-efficient representation learning method for learning features from high-dimensional images. In S2R, we introduce a representation learning framework and define a novel multi-view auxiliary objective based on the multi-view image states and Conditional Entropy Bottleneck (CEB) principle. We integrate S2R with the deep RL agent to learn robust representations that preserve task-relevant information while discarding task-irrelevant information and find optimal policies that maximize the expected return. Empirically, we demonstrate the effectiveness of S2R in the visual DeepMind Control (DMControl) suite and show its better performance on the default DMControl tasks and their variants by replacing the tasks’ default background with a random image or natural video.}
}


@inproceedings{AEVB,
 author = {Shuyang Gao and
Rob Brekelmans and
Greg Ver Steeg and
Aram Galstyan},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aistats/GaoBSG19.bib},
 booktitle = {The 22nd International Conference on Artificial Intelligence and Statistics,
{AISTATS} 2019, 16-18 April 2019, Naha, Okinawa, Japan},
 editor = {Kamalika Chaudhuri and
Masashi Sugiyama},
 pages = {1157--1166},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Fri, 07 Jun 2019 01:00:00 +0200},
 title = {Auto-Encoding Total Correlation Explanation},
 url = {http://proceedings.mlr.press/v89/gao19a.html},
 volume = {89},
 year = {2019}
}

@article{MIME,
 author = {Mohamed Ishmael Belghazi and Aristide Baratin and Sai Rajeswar and Sherjil Ozair and Yoshua Bengio and Aaron Courville and R Devon Hjelm},
 journal = {ArXiv preprint},
 title = {MINE: Mutual Information Neural Estimation},
 url = {https://arxiv.org/abs/1801.04062},
 volume = {abs/1801.04062},
 year = {2018}
}

@article{5392532,
 author = {Watanabe, Satosi},
 doi = {10.1147/rd.41.0066},
 journal = {IBM Journal of Research and Development},
 number = {1},
 pages = {66-82},
 title = {Information Theoretical Analysis of Multivariate Correlation},
 volume = {4},
 year = {1960}
}

@inproceedings{1410.7404,
 author = {Greg Ver Steeg and
Aram Galstyan},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aistats/SteegG15.bib},
 booktitle = {Proceedings of the Eighteenth International Conference on Artificial
Intelligence and Statistics, {AISTATS} 2015, San Diego, California,
USA, May 9-12, 2015},
 editor = {Guy Lebanon and
S. V. N. Vishwanathan},
 publisher = {JMLR.org},
 series = {{JMLR} Workshop and Conference Proceedings},
 timestamp = {Wed, 29 May 2019 01:00:00 +0200},
 title = {Maximally Informative Hierarchical Representations of High-Dimensional
Data},
 url = {http://proceedings.mlr.press/v38/versteeg15.html},
 volume = {38},
 year = {2015}
}

@inproceedings{1406.1222,
 author = {Greg Ver Steeg and
Aram Galstyan},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/SteegG14.bib},
 booktitle = {Advances in Neural Information Processing Systems 27: Annual Conference
on Neural Information Processing Systems 2014, December 8-13 2014,
Montreal, Quebec, Canada},
 editor = {Zoubin Ghahramani and
Max Welling and
Corinna Cortes and
Neil D. Lawrence and
Kilian Q. Weinberger},
 pages = {577--585},
 timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
 title = {Discovering Structure in High-Dimensional Data Through Correlation
Explanation},
 url = {https://proceedings.neurips.cc/paper/2014/hash/4f6ffe13a5d75b2d6a3923922b3922e5-Abstract.html},
 year = {2014}
}

@inproceedings{1802.05822,
 author = {Shuyang Gao and
Rob Brekelmans and
Greg Ver Steeg and
Aram Galstyan},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aistats/GaoBSG19.bib},
 booktitle = {The 22nd International Conference on Artificial Intelligence and Statistics,
{AISTATS} 2019, 16-18 April 2019, Naha, Okinawa, Japan},
 editor = {Kamalika Chaudhuri and
Masashi Sugiyama},
 pages = {1157--1166},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Fri, 07 Jun 2019 01:00:00 +0200},
 title = {Auto-Encoding Total Correlation Explanation},
 url = {http://proceedings.mlr.press/v89/gao19a.html},
 volume = {89},
 year = {2019}
}

@inproceedings{drq,
 author = {Denis Yarats and
Ilya Kostrikov and
Rob Fergus},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/iclr/YaratsKF21.bib},
 booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
Virtual Event, Austria, May 3-7, 2021},
 publisher = {OpenReview.net},
 timestamp = {Wed, 23 Jun 2021 01:00:00 +0200},
 title = {Image Augmentation Is All You Need: Regularizing Deep Reinforcement
Learning from Pixels},
 url = {https://openreview.net/forum?id=GY6-6sTvGaf},
 year = {2021}
}

@article{ivw01,
 author = {William G. Cochran},
 issn = {0006341X, 15410420},
 journal = {Biometrics},
 number = {1},
 pages = {101--129},
 publisher = {[Wiley, International Biometric Society]},
 title = {The Combination of Estimates from Different Experiments},
 url = {http://www.jstor.org/stable/3001666},
 urldate = {2022-11-29},
 volume = {10},
 year = {1954}
}

@article{ivw02,
 author = {William G. Cochran and Sarah Porter Carroll},
 issn = {0006341X, 15410420},
 journal = {Biometrics},
 number = {4},
 pages = {447--459},
 publisher = {[Wiley, International Biometric Society]},
 title = {A Sampling Investigation of the Efficiency of Weighting Inversely as the Estimated Variance},
 url = {http://www.jstor.org/stable/3001436},
 urldate = {2022-11-29},
 volume = {9},
 year = {1953}
}

@article{poe,
 abstract = {It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual "expert" models makes it hard to generate samples from the combined model but easy to infer the values of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary. Training a PoE by maximizing the likelihood of the data is difficult because it is hard even to approximate the derivatives of the renormalization term in the combination rule. Fortunately, a PoE can be trained using a different objective function called "contrastive divergence" whose derivatives with regard to the parameters can be approximated accurately and efficiently. Examples are presented of contrastive divergence learning using several types of expert on several types of data.},
 address = {Cambridge, MA, USA},
 author = {Hinton, Geoffrey E.},
 doi = {10.1162/089976602760128018},
 issn = {0899-7667},
 issue_date = {August 2002},
 journal = {Neural Comput.},
 number = {8},
 numpages = {30},
 pages = {1771–1800},
 publisher = {MIT Press},
 title = {Training Products of Experts by Minimizing Contrastive Divergence},
 url = {https://doi.org/10.1162/089976602760128018},
 volume = {14},
 year = {2002}
}

@misc{CEB,
 author = {Ian Fischer},
 journal = {ArXiv preprint},
 title = {The Conditional Entropy Bottleneck},
 url = {https://arxiv.org/abs/2002.05379},
 volume = {abs/2002.05379},
 year = {2020}
}

@inproceedings{SLAC,
 author = {Alex X. Lee and
Anusha Nagabandi and
Pieter Abbeel and
Sergey Levine},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/LeeNAL20.bib},
 booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
on Neural Information Processing Systems 2020, NeurIPS 2020, December
6-12, 2020, virtual},
 editor = {Hugo Larochelle and
Marc'Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
 timestamp = {Tue, 19 Jan 2021 00:00:00 +0100},
 title = {Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a
Latent Variable Model},
 url = {https://proceedings.neurips.cc/paper/2020/hash/08058bf500242562c0d031ff830ad094-Abstract.html},
 year = {2020}
}

@inproceedings{MVTC,
 author = {Hwang, HyeongJoo and Kim, Geon-Hyeong and Hong, Seunghoon and Kim, Kee-Eung},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {12194--12207},
 publisher = {Curran Associates, Inc.},
 title = {Multi-View Representation Learning via Total Correlation Objective},
 url = {https://proceedings.neurips.cc/paper/2021/file/65a99bb7a3115fdede20da98b08a370f-Paper.pdf},
 volume = {34},
 year = {2021}
}

@inproceedings{swav,
 author = {Mathilde Caron and
Ishan Misra and
Julien Mairal and
Priya Goyal and
Piotr Bojanowski and
Armand Joulin},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/CaronMMGBJ20.bib},
 booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
on Neural Information Processing Systems 2020, NeurIPS 2020, December
6-12, 2020, virtual},
 editor = {Hugo Larochelle and
Marc'Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
 timestamp = {Tue, 19 Jan 2021 00:00:00 +0100},
 title = {Unsupervised Learning of Visual Features by Contrasting Cluster Assignments},
 url = {https://proceedings.neurips.cc/paper/2020/hash/70feb62b69f16e0238f741fab228fec2-Abstract.html},
 year = {2020}
}

@article{NatureMatMul,
 abstract = {Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems---from neural networks to scientific computing routines. The automatic discovery of algorithms using machine learning offers the prospect of reaching beyond human intuition and outperforming the current best human-designed algorithms. However, automating the algorithm discovery procedure is intricate, as the space of possible algorithms is enormous. Here we report a deep reinforcement learning approach based on AlphaZero1 for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices. Our agent, AlphaTensor, is trained to play a single-player game where the objective is finding tensor decompositions within a finite factor space. AlphaTensor discovered algorithms that outperform the state-of-the-art complexity for many matrix sizes. Particularly relevant is the case of 4{\thinspace}{\texttimes}{\thinspace}4 matrices in a finite field, where AlphaTensor's algorithm improves on Strassen's two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2. We further showcase the flexibility of AlphaTensor through different use-cases: algorithms with state-of-the-art complexity for structured matrix multiplication and improved practical efficiency by optimizing matrix multiplication for runtime on specific hardware. Our results highlight AlphaTensor's ability to accelerate the process of algorithmic discovery on a range of problems, and to optimize for different criteria.},
 author = {Fawzi, Alhussein
and Balog, Matej
and Huang, Aja
and Hubert, Thomas
and Romera-Paredes, Bernardino
and Barekatain, Mohammadamin
and Novikov, Alexander
and R. Ruiz, Francisco J.
and Schrittwieser, Julian
and Swirszcz, Grzegorz
and Silver, David
and Hassabis, Demis
and Kohli, Pushmeet},
 day = {01},
 doi = {10.1038/s41586-022-05172-4},
 issn = {1476-4687},
 journal = {Nature},
 number = {7930},
 pages = {47-53},
 title = {Discovering faster matrix multiplication algorithms with reinforcement learning},
 url = {https://doi.org/10.1038/s41586-022-05172-4},
 volume = {610},
 year = {2022}
}

@inproceedings{sacori,
 author = {Tuomas Haarnoja and
Aurick Zhou and
Pieter Abbeel and
Sergey Levine},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/HaarnojaZAL18.bib},
 booktitle = {Proceedings of the 35th International Conference on Machine Learning,
{ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July 10-15,
2018},
 editor = {Jennifer G. Dy and
Andreas Krause},
 pages = {1856--1865},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Wed, 03 Apr 2019 01:00:00 +0200},
 title = {Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning
with a Stochastic Actor},
 url = {http://proceedings.mlr.press/v80/haarnoja18b.html},
 volume = {80},
 year = {2018}
}

@inproceedings{td3ori,
 author = {Scott Fujimoto and
Herke van Hoof and
David Meger},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/FujimotoHM18.bib},
 booktitle = {Proceedings of the 35th International Conference on Machine Learning,
{ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July 10-15,
2018},
 editor = {Jennifer G. Dy and
Andreas Krause},
 pages = {1582--1591},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Wed, 03 Apr 2019 01:00:00 +0200},
 title = {Addressing Function Approximation Error in Actor-Critic Methods},
 url = {http://proceedings.mlr.press/v80/fujimoto18a.html},
 volume = {80},
 year = {2018}
}

@inproceedings{ddpgori,
 author = {David Silver and
Guy Lever and
Nicolas Heess and
Thomas Degris and
Daan Wierstra and
Martin A. Riedmiller},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/SilverLHDWR14.bib},
 booktitle = {Proceedings of the 31th International Conference on Machine Learning,
{ICML} 2014, Beijing, China, 21-26 June 2014},
 pages = {387--395},
 publisher = {JMLR.org},
 series = {{JMLR} Workshop and Conference Proceedings},
 timestamp = {Wed, 29 May 2019 01:00:00 +0200},
 title = {Deterministic Policy Gradient Algorithms},
 url = {http://proceedings.mlr.press/v32/silver14.html},
 volume = {32},
 year = {2014}
}

@misc{SAC-AE,
 author = {Denis Yarats and Amy Zhang and Ilya Kostrikov and Brandon Amos and Joelle Pineau and Rob Fergus},
 journal = {ArXiv preprint},
 title = {Improving Sample Efficiency in Model-Free Reinforcement Learning from Images},
 url = {https://arxiv.org/abs/1910.01741},
 volume = {abs/1910.01741},
 year = {2019}
}

@inproceedings{TemporalCoherence,
 author = {Will Y. Zou},
 title = {Unsupervised learning of visual invariance with temporal coherence},
 year = {2011}
}

@inproceedings{IsoManLearn,
 author = {Pai, Gautam and Talmon, Ronen and Bronstein, Alex and Kimmel, Ron},
 doi = {10.1109/WACV.2019.00092},
 pages = {},
 title = {DIMAL: Deep Isometric Manifold Learning Using Sparse Geodesic Sampling},
 year = {2018}
}

@inproceedings{2006.16992,
 author = {Haozhi Qi and
Chong You and
Xiaolong Wang and
Yi Ma and
Jitendra Malik},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/QiYWMM20.bib},
 booktitle = {Proceedings of the 37th International Conference on Machine Learning,
{ICML} 2020, 13-18 July 2020, Virtual Event},
 pages = {7824--7835},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Tue, 15 Dec 2020 00:00:00 +0100},
 title = {Deep Isometric Learning for Visual Recognition},
 url = {http://proceedings.mlr.press/v119/qi20a.html},
 volume = {119},
 year = {2020}
}

@article{feature-stack,
 abstract = {Deep multi-layer neural networks have many levels of non-linearities allowing them to compactly represent highly non-linear and highly-varying functions. However, until recently it was not clear how to train such deep networks, since gradient-based optimization starting from random initialization often appears to get stuck in poor solutions. Hinton et al. recently proposed a greedy layer-wise unsupervised learning procedure relying on the training algorithm of restricted Boltzmann machines (RBM) to initialize the parameters of a deep belief network (DBN), a generative model with many layers of hidden causal variables. This was followed by the proposal of another greedy layer-wise procedure, relying on the usage of autoassociator networks. In the context of the above optimization problem, we study these algorithms empirically to better understand their success. Our experiments confirm the hypothesis that the greedy layer-wise unsupervised training strategy helps the optimization by initializing weights in a region near a good local minimum, but also implicitly acts as a sort of regularization that brings better generalization and encourages internal distributed representations that are high-level abstractions of the input. We also present a series of experiments aimed at evaluating the link between the performance of deep neural networks and practical aspects of their topology, for example, demonstrating cases where the addition of more depth helps. Finally, we empirically explore simple variants of these training algorithms, such as the use of different RBM input unit distributions, a simple way of combining gradient estimators to improve performance, as well as on-line versions of those algorithms.},
 author = {Larochelle, Hugo and Bengio, Yoshua and Louradour, J\'{e}r\^{o}me and Lamblin, Pascal},
 issn = {1532-4435},
 issue_date = {12/1/2009},
 journal = {J. Mach. Learn. Res.},
 numpages = {40},
 pages = {1–40},
 publisher = {JMLR.org},
 title = {Exploring Strategies for Training Deep Neural Networks},
 volume = {10},
 year = {2009}
}

@misc{rep-survey,
 author = {Yoshua Bengio and Aaron Courville and Pascal Vincent},
 eprint = {arXiv:1206.5538},
 title = {Representation Learning: A Review and New Perspectives},
 year = {2012}
}

@inproceedings{ProbRSSM,
 author = {Andreas Doerr and
Christian Daniel and
Martin Schiegg and
Duy Nguyen{-}Tuong and
Stefan Schaal and
Marc Toussaint and
Sebastian Trimpe},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/DoerrDSNSTT18.bib},
 booktitle = {Proceedings of the 35th International Conference on Machine Learning,
{ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July 10-15,
2018},
 editor = {Jennifer G. Dy and
Andreas Krause},
 pages = {1279--1288},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Wed, 03 Apr 2019 01:00:00 +0200},
 title = {Probabilistic Recurrent State-Space Models},
 url = {http://proceedings.mlr.press/v80/doerr18a.html},
 volume = {80},
 year = {2018}
}

@misc{HiddenRSSM,
 author = {Vaisakh Shaj and Dieter Buchler and Rohit Sonker and Philipp Becker and Gerhard Neumann},
 journal = {ArXiv preprint},
 title = {Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios},
 url = {https://arxiv.org/abs/2206.14697},
 volume = {abs/2206.14697},
 year = {2022}
}

@inproceedings{QuantifyGeneralizationRL,
 author = {Karl Cobbe and
Oleg Klimov and
Christopher Hesse and
Taehoon Kim and
John Schulman},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/CobbeKHKS19.bib},
 booktitle = {Proceedings of the 36th International Conference on Machine Learning,
{ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
 editor = {Kamalika Chaudhuri and
Ruslan Salakhutdinov},
 pages = {1282--1289},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Tue, 11 Jun 2019 01:00:00 +0200},
 title = {Quantifying Generalization in Reinforcement Learning},
 url = {http://proceedings.mlr.press/v97/cobbe19a.html},
 volume = {97},
 year = {2019}
}

@inproceedings{netrand,
 author = {Kimin Lee and
Kibok Lee and
Jinwoo Shin and
Honglak Lee},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/iclr/LeeLSL20.bib},
 booktitle = {8th International Conference on Learning Representations, {ICLR} 2020,
Addis Ababa, Ethiopia, April 26-30, 2020},
 publisher = {OpenReview.net},
 timestamp = {Thu, 07 May 2020 01:00:00 +0200},
 title = {Network Randomization: {A} Simple Technique for Generalization in
Deep Reinforcement Learning},
 url = {https://openreview.net/forum?id=HJgcvJBFvB},
 year = {2020}
}

@article{KalmenFilter,
 author = {Kalman, Rudolph Emil},
 journal = {Transactions of the ASME--Journal of Basic Engineering},
 number = {Series D},
 pages = {35--45},
 title = {A New Approach to Linear Filtering and Prediction Problems},
 volume = {82},
 year = {1960}
}

@inproceedings{bisim_knowledge_transfer,
 author = {Pablo Samuel Castro and
Doina Precup},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aaai/CastroP10.bib},
 booktitle = {Proceedings of the Twenty-Fourth {AAAI} Conference on Artificial Intelligence,
{AAAI} 2010, Atlanta, Georgia, USA, July 11-15, 2010},
 editor = {Maria Fox and
David Poole},
 publisher = {{AAAI} Press},
 timestamp = {Wed, 01 Sep 2010 01:00:00 +0200},
 title = {Using Bisimulation for Policy Transfer in MDPs},
 url = {http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1907},
 year = {2010}
}

@misc{DARL,
 author = {Bonnie Li and Vincent François-Lavet and Thang Doan and Joelle Pineau},
 journal = {ArXiv preprint},
 title = {Domain Adversarial Reinforcement Learning},
 url = {https://arxiv.org/abs/2102.07097},
 volume = {abs/2102.07097},
 year = {2021}
}

@inproceedings{IAPE,
 author = {Mart{\'{\i}}n Bertr{\'{a}}n and
Natalia Mart{\'{\i}}nez and
Mariano Phielipp and
Guillermo Sapiro},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/BertranMPS20.bib},
 booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
on Neural Information Processing Systems 2020, NeurIPS 2020, December
6-12, 2020, virtual},
 editor = {Hugo Larochelle and
Marc'Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
 timestamp = {Tue, 19 Jan 2021 00:00:00 +0100},
 title = {Instance-based Generalization in Reinforcement Learning},
 url = {https://proceedings.neurips.cc/paper/2020/hash/82674fc29bc0d9895cee346548c2cb5c-Abstract.html},
 year = {2020}
}

@article{IPO,
 author = {{Sonar}, Anoopkumar and {Pacelli}, Vincent and {Majumdar}, Anirudha},
 journal = {ArXiv preprint},
 title = {{Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning}},
 url = {https://arxiv.org/abs/2006.01096},
 volume = {abs/2006.01096},
 year = {2020}
}

@inproceedings{MISA,
 author = {Amy Zhang and
Clare Lyle and
Shagun Sodhani and
Angelos Filos and
Marta Kwiatkowska and
Joelle Pineau and
Yarin Gal and
Doina Precup},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/0001LSFKPGP20.bib},
 booktitle = {Proceedings of the 37th International Conference on Machine Learning,
{ICML} 2020, 13-18 July 2020, Virtual Event},
 pages = {11214--11224},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Tue, 15 Dec 2020 00:00:00 +0100},
 title = {Invariant Causal Prediction for Block MDPs},
 url = {http://proceedings.mlr.press/v119/zhang20t.html},
 volume = {119},
 year = {2020}
}

@inproceedings{protoRL,
 author = {Denis Yarats and
Rob Fergus and
Alessandro Lazaric and
Lerrel Pinto},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/YaratsFLP21.bib},
 booktitle = {Proceedings of the 38th International Conference on Machine Learning,
{ICML} 2021, 18-24 July 2021, Virtual Event},
 editor = {Marina Meila and
Tong Zhang},
 pages = {11920--11931},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Wed, 25 Aug 2021 01:00:00 +0200},
 title = {Reinforcement Learning with Prototypical Representations},
 url = {http://proceedings.mlr.press/v139/yarats21a.html},
 volume = {139},
 year = {2021}
}

@article{LEEP,
 author = {{Ghosh}, Dibya and {Rahme}, Jad and {Kumar}, Aviral and {Zhang}, Amy and {Adams}, Ryan P. and {Levine}, Sergey},
 journal = {ArXiv preprint},
 title = {{Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability}},
 url = {https://arxiv.org/abs/2107.06277},
 volume = {abs/2107.06277},
 year = {2021}
}

@inproceedings{DAAC,
 author = {Roberta Raileanu and
Rob Fergus},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/RaileanuF21.bib},
 booktitle = {Proceedings of the 38th International Conference on Machine Learning,
{ICML} 2021, 18-24 July 2021, Virtual Event},
 editor = {Marina Meila and
Tong Zhang},
 pages = {8787--8798},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Wed, 25 Aug 2021 01:00:00 +0200},
 title = {Decoupling Value and Policy for Generalization in Reinforcement Learning},
 url = {http://proceedings.mlr.press/v139/raileanu21a.html},
 volume = {139},
 year = {2021}
}

@misc{CTRL,
 author = {Bogdan Mazoure and Ahmed M. Ahmed and Patrick MacAlpine and R Devon Hjelm and Andrey Kolobov},
 journal = {ArXiv preprint},
 title = {Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL},
 url = {https://arxiv.org/abs/2106.02193},
 volume = {abs/2106.02193},
 year = {2021}
}

@inproceedings{BisimGoal,
 abstract = {Building generalizable goal-conditioned agents from rich observations is a key to reinforcement learning (RL) solving real world problems. Traditionally in goal-conditioned RL, an agent is provided with the exact goal they intend to reach. However, it is often not realistic to know the configuration of the goal before performing a task. A more scalable framework would allow us to provide the agent with an example of an analogous task, and have the agent then infer what the goal should be for its current state. We propose a new form of state abstraction called goal-conditioned bisimulation that captures functional equivariance, allowing for the reuse of skills to achieve new goals. We learn this representation using a metric form of this abstraction, and show its ability to generalize to new goals in real world manipulation tasks. Further, we prove that this learned representation is sufficient not only for goal-conditioned tasks, but is amenable to any downstream task described by a state-only reward function.},
 author = {Hansen-Estruch, Philippe and Zhang, Amy and Nair, Ashvin and Yin, Patrick and Levine, Sergey},
 booktitle = {Proceedings of the 39th International Conference on Machine Learning},
 editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
 pages = {8407--8426},
 pdf = {https://proceedings.mlr.press/v162/hansen-estruch22a/hansen-estruch22a.pdf},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 title = {Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning},
 url = {https://proceedings.mlr.press/v162/hansen-estruch22a.html},
 volume = {162},
 year = {2022}
}

@inproceedings{PISAC,
 author = {Kuang{-}Huei Lee and
Ian Fischer and
Anthony Liu and
Yijie Guo and
Honglak Lee and
John Canny and
Sergio Guadarrama},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/LeeFLGLCG20.bib},
 booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
on Neural Information Processing Systems 2020, NeurIPS 2020, December
6-12, 2020, virtual},
 editor = {Hugo Larochelle and
Marc'Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
 timestamp = {Tue, 19 Jan 2021 00:00:00 +0100},
 title = {Predictive Information Accelerates Learning in {RL}},
 url = {https://proceedings.neurips.cc/paper/2020/hash/89b9e0a6f6d1505fe13dea0f18a2dcfa-Abstract.html},
 year = {2020}
}

@inproceedings{RAD,
 author = {Michael Laskin and
Kimin Lee and
Adam Stooke and
Lerrel Pinto and
Pieter Abbeel and
Aravind Srinivas},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/LaskinLSPAS20.bib},
 booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
on Neural Information Processing Systems 2020, NeurIPS 2020, December
6-12, 2020, virtual},
 editor = {Hugo Larochelle and
Marc'Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
 timestamp = {Tue, 19 Jan 2021 00:00:00 +0100},
 title = {Reinforcement Learning with Augmented Data},
 url = {https://proceedings.neurips.cc/paper/2020/hash/e615c82aba461681ade82da2da38004a-Abstract.html},
 year = {2020}
}

@inproceedings{dreamerv2,
 author = {Danijar Hafner and
Timothy P. Lillicrap and
Mohammad Norouzi and
Jimmy Ba},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/iclr/HafnerL0B21.bib},
 booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
Virtual Event, Austria, May 3-7, 2021},
 publisher = {OpenReview.net},
 timestamp = {Wed, 23 Jun 2021 01:00:00 +0200},
 title = {Mastering Atari with Discrete World Models},
 url = {https://openreview.net/forum?id=0oabwyZbOu},
 year = {2021}
}

@inproceedings{Reparamterization,
 author = {Diederik P. Kingma and
Max Welling},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/journals/corr/KingmaW13.bib},
 booktitle = {2nd International Conference on Learning Representations, {ICLR} 2014,
Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings},
 editor = {Yoshua Bengio and
Yann LeCun},
 timestamp = {Fri, 29 Mar 2019 00:00:00 +0100},
 title = {Auto-Encoding Variational Bayes},
 url = {http://arxiv.org/abs/1312.6114},
 year = {2014}
}

@misc{CPCInfoNCE,
 author = {Aaron van den Oord and Yazhe Li and Oriol Vinyals},
 journal = {ArXiv preprint},
 title = {Representation Learning with Contrastive Predictive Coding},
 url = {https://arxiv.org/abs/1807.03748},
 volume = {abs/1807.03748},
 year = {2018}
}

@inproceedings{DBCIRID,
 author = {Kemertas, Mete and Aumentado-Armstrong, Tristan},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {4764--4777},
 publisher = {Curran Associates, Inc.},
 title = {Towards Robust Bisimulation Metric Learning},
 url = {https://proceedings.neurips.cc/paper/2021/file/256bf8e6923a52fda8ddf7dc050a1148-Paper.pdf},
 volume = {34},
 year = {2021}
}

@inproceedings{CURL,
 author = {Michael Laskin and
Aravind Srinivas and
Pieter Abbeel},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/LaskinSA20.bib},
 booktitle = {Proceedings of the 37th International Conference on Machine Learning,
{ICML} 2020, 13-18 July 2020, Virtual Event},
 pages = {5639--5650},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Tue, 15 Dec 2020 00:00:00 +0100},
 title = {{CURL:} Contrastive Unsupervised Representations for Reinforcement
Learning},
 url = {http://proceedings.mlr.press/v119/laskin20a.html},
 volume = {119},
 year = {2020}
}

@inproceedings{MultiViewIBCV,
 author = {Marco Federici and
Anjan Dutta and
Patrick Forr{\'{e}} and
Nate Kushman and
Zeynep Akata},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/iclr/Federici0FKA20.bib},
 booktitle = {8th International Conference on Learning Representations, {ICLR} 2020,
Addis Ababa, Ethiopia, April 26-30, 2020},
 publisher = {OpenReview.net},
 timestamp = {Thu, 07 May 2020 01:00:00 +0200},
 title = {Learning Robust Representations via Multi-View Information Bottleneck},
 url = {https://openreview.net/forum?id=B1xwcyHFDr},
 year = {2020}
}

@inproceedings{RSSM,
 author = {Danijar Hafner and
Timothy P. Lillicrap and
Ian Fischer and
Ruben Villegas and
David Ha and
Honglak Lee and
James Davidson},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/HafnerLFVHLD19.bib},
 booktitle = {Proceedings of the 36th International Conference on Machine Learning,
{ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
 editor = {Kamalika Chaudhuri and
Ruslan Salakhutdinov},
 pages = {2555--2565},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Tue, 11 Jun 2019 01:00:00 +0200},
 title = {Learning Latent Dynamics for Planning from Pixels},
 url = {http://proceedings.mlr.press/v97/hafner19a.html},
 volume = {97},
 year = {2019}
}

@misc{SurveyZeroShot,
 author = {Robert Kirk and Amy Zhang and Edward Grefenstette and Tim Rocktäschel},
 journal = {ArXiv preprint},
 title = {A Survey of Generalisation in Deep Reinforcement Learning},
 url = {https://arxiv.org/abs/2111.09794},
 volume = {abs/2111.09794},
 year = {2021}
}

@inproceedings{PlaNet,
 author = {Danijar Hafner and
Timothy P. Lillicrap and
Ian Fischer and
Ruben Villegas and
David Ha and
Honglak Lee and
James Davidson},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/HafnerLFVHLD19.bib},
 booktitle = {Proceedings of the 36th International Conference on Machine Learning,
{ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
 editor = {Kamalika Chaudhuri and
Ruslan Salakhutdinov},
 pages = {2555--2565},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Tue, 11 Jun 2019 01:00:00 +0200},
 title = {Learning Latent Dynamics for Planning from Pixels},
 url = {http://proceedings.mlr.press/v97/hafner19a.html},
 volume = {97},
 year = {2019}
}

@inproceedings{DRIBO,
 abstract = {Deep reinforcement learning (DRL) agents are often sensitive to visual changes that were unseen in their training environments. To address this problem, we leverage the sequential nature of RL to learn robust representations that encode only task-relevant information from observations based on the unsupervised multi-view setting. Specif- ically, we introduce a novel contrastive version of the Multi-View Information Bottleneck (MIB) objective for temporal data. We train RL agents from pixels with this auxiliary objective to learn robust representations that can compress away task-irrelevant information and are predictive of task-relevant dynamics. This approach enables us to train high-performance policies that are robust to visual distractions and can generalize well to unseen environments. We demonstrate that our approach can achieve SOTA performance on a di- verse set of visual control tasks in the DeepMind Control Suite when the background is replaced with natural videos. In addition, we show that our approach outperforms well-established base- lines for generalization to unseen environments on the Procgen benchmark. Our code is open- sourced and available at https://github. com/BU-DEPEND-Lab/DRIBO.},
 author = {Fan, Jiameng and Li, Wenchao},
 booktitle = {Proceedings of the 39th International Conference on Machine Learning},
 editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
 pages = {6074--6102},
 pdf = {https://proceedings.mlr.press/v162/fan22b/fan22b.pdf},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 title = {{DRIBO}: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck},
 url = {https://proceedings.mlr.press/v162/fan22b.html},
 volume = {162},
 year = {2022}
}

@misc{distracting_control_suite,
 author = {Austin Stone and Oscar Ramirez and Kurt Konolige and Rico Jonschkowski},
 journal = {ArXiv preprint},
 title = {The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels},
 url = {https://arxiv.org/abs/2101.02722},
 volume = {abs/2101.02722},
 year = {2021}
}

@misc{jumping_task,
 author = {Remi Tachet and Philip Bachman and Harm van Seijen},
 journal = {ArXiv preprint},
 title = {Learning Invariances for Policy Generalization},
 url = {https://arxiv.org/abs/1809.02591},
 volume = {abs/1809.02591},
 year = {2018}
}

@misc{Villani08references,
 author = {Cédric Villani},
 title = {Topics in Optimal Transportation [TOT] (AMS,},
 year = {2008}
}

@inproceedings{PSE,
 author = {Rishabh Agarwal and
Marlos C. Machado and
Pablo Samuel Castro and
Marc G. Bellemare},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/iclr/AgarwalMCB21.bib},
 booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
Virtual Event, Austria, May 3-7, 2021},
 publisher = {OpenReview.net},
 timestamp = {Wed, 23 Jun 2021 01:00:00 +0200},
 title = {Contrastive Behavioral Similarity Embeddings for Generalization in
Reinforcement Learning},
 url = {https://openreview.net/forum?id=qda7-sVg84},
 year = {2021}
}

@misc{Deepmind_control_suite,
 author = {Tassa, Yuval and Doron, Yotam and Muldal, Alistair and Erez, Tom and Li, Yazhe and Casas, Diego de Las and Budden, David and Abdolmaleki, Abbas and Merel, Josh and Lefrancq, Andrew and Lillicrap, Timothy and Riedmiller, Martin},
 journal = {ArXiv preprint},
 title = {DeepMind Control Suite},
 url = {https://arxiv.org/abs/1801.00690},
 volume = {abs/1801.00690},
 year = {2018}
}

@inproceedings{1807.03858,
 author = {Yuping Luo and
Huazhe Xu and
Yuanzhi Li and
Yuandong Tian and
Trevor Darrell and
Tengyu Ma},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/iclr/LuoXLTDM19.bib},
 booktitle = {7th International Conference on Learning Representations, {ICLR} 2019,
New Orleans, LA, USA, May 6-9, 2019},
 publisher = {OpenReview.net},
 timestamp = {Thu, 25 Jul 2019 01:00:00 +0200},
 title = {Algorithmic Framework for Model-based Deep Reinforcement Learning
with Theoretical Guarantees},
 url = {https://openreview.net/forum?id=BJe1E2R5KX},
 year = {2019}
}

@inproceedings{1703.00443,
 author = {Brandon Amos and
J. Zico Kolter},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/AmosK17.bib},
 booktitle = {Proceedings of the 34th International Conference on Machine Learning,
{ICML} 2017, Sydney, NSW, Australia, 6-11 August 2017},
 editor = {Doina Precup and
Yee Whye Teh},
 pages = {136--145},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Wed, 03 Apr 2019 01:00:00 +0200},
 title = {OptNet: Differentiable Optimization as a Layer in Neural Networks},
 url = {http://proceedings.mlr.press/v70/amos17a.html},
 volume = {70},
 year = {2017}
}

@inproceedings{1705.05363,
 author = {Deepak Pathak and
Pulkit Agrawal and
Alexei A. Efros and
Trevor Darrell},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/PathakAED17.bib},
 booktitle = {Proceedings of the 34th International Conference on Machine Learning,
{ICML} 2017, Sydney, NSW, Australia, 6-11 August 2017},
 editor = {Doina Precup and
Yee Whye Teh},
 pages = {2778--2787},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Wed, 03 Apr 2019 01:00:00 +0200},
 title = {Curiosity-driven Exploration by Self-supervised Prediction},
 url = {http://proceedings.mlr.press/v70/pathak17a.html},
 volume = {70},
 year = {2017}
}

@inproceedings{1809.04506,
 author = {Vincent Fran{\c{c}}ois{-}Lavet and
Yoshua Bengio and
Doina Precup and
Joelle Pineau},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aaai/Francois-LavetB19.bib},
 booktitle = {The Thirty-Third {AAAI} Conference on Artificial Intelligence, {AAAI}
2019, The Thirty-First Innovative Applications of Artificial Intelligence
Conference, {IAAI} 2019, The Ninth {AAAI} Symposium on Educational
Advances in Artificial Intelligence, {EAAI} 2019, Honolulu, Hawaii,
USA, January 27 - February 1, 2019},
 doi = {10.1609/aaai.v33i01.33013582},
 pages = {3582--3589},
 publisher = {{AAAI} Press},
 timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
 title = {Combined Reinforcement Learning via Abstract Representations},
 url = {https://doi.org/10.1609/aaai.v33i01.33013582},
 year = {2019}
}

@misc{1612.07307,
 author = {Evan Shelhamer and Parsa Mahmoudieh and Max Argus and Trevor Darrell},
 journal = {ArXiv preprint},
 title = {Loss is its own Reward: Self-Supervision for Reinforcement Learning},
 url = {https://arxiv.org/abs/1612.07307},
 volume = {abs/1612.07307},
 year = {2016}
}

@misc{1705.07269,
 author = {Sahil Sharma and Aravind Suresh and Rahul Ramesh and Balaraman Ravindran},
 journal = {ArXiv preprint},
 title = {Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning},
 url = {https://arxiv.org/abs/1705.07269},
 volume = {abs/1705.07269},
 year = {2017}
}

@inproceedings{1902.01119,
 author = {Guy Tennenholtz and
Shie Mannor},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/TennenholtzM19.bib},
 booktitle = {Proceedings of the 36th International Conference on Machine Learning,
{ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
 editor = {Kamalika Chaudhuri and
Ruslan Salakhutdinov},
 pages = {6196--6205},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Tue, 11 Jun 2019 01:00:00 +0200},
 title = {The Natural Language of Actions},
 url = {http://proceedings.mlr.press/v97/tennenholtz19a.html},
 volume = {97},
 year = {2019}
}

@inproceedings{GeneralizedV,
 author = {Jason Pazis and
Ronald Parr},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/PazisP11.bib},
 booktitle = {Proceedings of the 28th International Conference on Machine Learning,
{ICML} 2011, Bellevue, Washington, USA, June 28 - July 2, 2011},
 editor = {Lise Getoor and
Tobias Scheffer},
 pages = {1185--1192},
 publisher = {Omnipress},
 timestamp = {Wed, 03 Apr 2019 01:00:00 +0200},
 title = {Generalized Value Functions for Large Action Sets},
 url = {https://icml.cc/2011/papers/609\_icmlpaper.pdf},
 year = {2011}
}

@misc{1512.07679,
 author = {Gabriel Dulac-Arnold and Richard Evans and Hado van Hasselt and Peter Sunehag and Timothy Lillicrap and Jonathan Hunt and Timothy Mann and Theophane Weber and Thomas Degris and Ben Coppin},
 journal = {ArXiv preprint},
 title = {Deep Reinforcement Learning in Large Discrete Action Spaces},
 url = {https://arxiv.org/abs/1512.07679},
 volume = {abs/1512.07679},
 year = {2015}
}

@inproceedings{pmlr-v97-chandak19a,
 author = {Yash Chandak and
Georgios Theocharous and
James Kostas and
Scott M. Jordan and
Philip S. Thomas},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/ChandakTKJT19.bib},
 booktitle = {Proceedings of the 36th International Conference on Machine Learning,
{ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
 editor = {Kamalika Chaudhuri and
Ruslan Salakhutdinov},
 pages = {941--950},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Thu, 13 Jun 2019 01:00:00 +0200},
 title = {Learning Action Representations for Reinforcement Learning},
 url = {http://proceedings.mlr.press/v97/chandak19a.html},
 volume = {97},
 year = {2019}
}

@misc{2107.07599,
 author = {Hanna Kurniawati},
 journal = {ArXiv preprint},
 title = {Partially Observable Markov Decision Processes (POMDPs) and Robotics},
 url = {https://arxiv.org/abs/2107.07599},
 volume = {abs/2107.07599},
 year = {2021}
}

@inproceedings{OptLayer,
 author = {Pham, Tu-Hoa and De Magistris, Giovanni and Tachibana, Ryuki},
 booktitle = {2018 IEEE International Conference on Robotics and Automation (ICRA)},
 doi = {10.1109/ICRA.2018.8460547},
 number = {},
 pages = {6236-6243},
 title = {OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World},
 volume = {},
 year = {2018}
}

@misc{Altman95constrainedmarkov,
 author = {Eitan Altman},
 title = {Constrained Markov Decision Processes},
 year = {1995}
}

@inproceedings{1705.10528,
 author = {Joshua Achiam and
David Held and
Aviv Tamar and
Pieter Abbeel},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/AchiamHTA17.bib},
 booktitle = {Proceedings of the 34th International Conference on Machine Learning,
{ICML} 2017, Sydney, NSW, Australia, 6-11 August 2017},
 editor = {Doina Precup and
Yee Whye Teh},
 pages = {22--31},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Wed, 03 Apr 2019 01:00:00 +0200},
 title = {Constrained Policy Optimization},
 url = {http://proceedings.mlr.press/v70/achiam17a.html},
 volume = {70},
 year = {2017}
}

@article{2101.09505,
 author = {Youngmin Kim and Richard Allmendinger and Manuel López-Ibáñez},
 journal = {ArXiv preprint},
 title = {Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art},
 url = {https://arxiv.org/abs/2101.09505},
 volume = {abs/2101.09505},
 year = {2021}
}

@inproceedings{1606.03476,
 author = {Jonathan Ho and
Stefano Ermon},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/HoE16.bib},
 booktitle = {Advances in Neural Information Processing Systems 29: Annual Conference
on Neural Information Processing Systems 2016, December 5-10, 2016,
Barcelona, Spain},
 editor = {Daniel D. Lee and
Masashi Sugiyama and
Ulrike von Luxburg and
Isabelle Guyon and
Roman Garnett},
 pages = {4565--4573},
 timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
 title = {Generative Adversarial Imitation Learning},
 url = {https://proceedings.neurips.cc/paper/2016/hash/cc7e2b878868cbae992d1fb743995d8f-Abstract.html},
 year = {2016}
}

@book{SuttonBook,
 abstract = {From the Publisher:In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.},
 address = {Cambridge, MA, USA},
 author = {Sutton, Richard S. and Barto, Andrew G.},
 edition = {1st},
 isbn = {0262193981},
 publisher = {MIT Press},
 title = {Introduction to Reinforcement Learning},
 year = {1998}
}

@inproceedings{RBFMethod,
 author = {Shibuya, Takeshi and Arita, Hideaki and Hamagami, Tomoki},
 doi = {10.1109/ICSMC.2010.5642294},
 journal = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
 pages = {1799-1803},
 title = {Reinforcement learning in continuous state space with perceptual aliasing by using complex-valued RBF network},
 year = {2010}
}

@article{Whitehead1990LearningTP,
 abstract = {This article considers adaptive control architectures that integrate active sensory-motor systems with decision systems based on reinforcement learning. One unavoidable consequence of active perception is that the agent's internal representation often confounds external world states. We call this phoenomenon Perceptual aliasing and show that it destabilizes existing reinforcement learning algorithms with respect to the optimal decision policy. We then describe a new decision system that overcomes these difficulties for a restricted class of decision problems. The system incorporates a perceptual subcycle within the overall decision cycle and uses a modified learning algorithm to suppress the effects of perceptual aliasing. The result is a control architecture that learns not only how to solve a task but also where to focus its visual attention in order to collect necessary sensory information.},
 author = {Whitehead, Steven D.
and Ballard, Dana H.},
 day = {01},
 doi = {10.1007/BF00058926},
 issn = {1573-0565},
 journal = {Machine Learning},
 number = {1},
 pages = {45-83},
 title = {Learning to perceive and act by trial and error},
 url = {https://doi.org/10.1007/BF00058926},
 volume = {7},
 year = {1991}
}

@inproceedings{Chrisman92reinforcementlearning,
 author = {Lonnie Chrisman},
 booktitle = {In Proceedings of the Tenth National Conference on Artificial Intelligence},
 pages = {183--188},
 publisher = {AAAI Press},
 title = {Reinforcement Learning with Perceptual Aliasing: The Perceptual Distinctions Approach},
 year = {1992}
}

@article{PMID:30307878,
 abstract = {Due to the lack of enough generalization in the state space, common methods of reinforcement learning suffer from slow learning speed, especially in the early learning trials. This paper introduces a model-based method in discrete state spaces for increasing the learning speed in terms of required experiences (but not required computation time) by exploiting generalization in the experiences of the subspaces. A subspace is formed by choosing a subset of features in the original state representation. Generalization and faster learning in a subspace are due to many-to-one mapping of experiences from the state space to each state in the subspace. Nevertheless, due to inherent perceptual aliasing (PA) in the subspaces, the policy suggested by each subspace does not generally converge to the optimal policy. Our approach, called model-based learning with subspaces (MoBLeSs), calculates the confidence intervals of the estimated Q -values in the state space and in the subspaces. These confidence intervals are used in the decision-making, such that the agent benefits the most from the possible generalization while avoiding from the detriment of the PA in the subspaces. The convergence of MoBLeS to the optimal policy is theoretically investigated. In addition, we show through several experiments that MoBLeS improves the learning speed in the early trials.},
 author = {Hashemzadeh, Maryam and Hosseini, Reshad and Ahmadabadi, Majid Nili},
 doi = {10.1109/tnnls.2018.2869978},
 issn = {2162-237X},
 journal = {IEEE transactions on neural networks and learning systems},
 number = {6},
 pages = {1635—1650},
 title = {Exploiting Generalization in the Subspaces for Faster Model-Based Reinforcement Learning},
 url = {https://doi.org/10.1109/TNNLS.2018.2869978},
 volume = {30},
 year = {2019}
}

@misc{2012.07091,
 author = {Milad Ghorbani and Reshad Hosseini and Seyed Pooya Shariatpanahi and Majid Nili Ahmadabadi},
 journal = {ArXiv preprint},
 title = {Reinforcement Learning with Subspaces using Free Energy Paradigm},
 url = {https://arxiv.org/abs/2012.07091},
 volume = {abs/2012.07091},
 year = {2020}
}

@inproceedings{DeepMDP,
 author = {Carles Gelada and
Saurabh Kumar and
Jacob Buckman and
Ofir Nachum and
Marc G. Bellemare},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/GeladaKBNB19.bib},
 booktitle = {Proceedings of the 36th International Conference on Machine Learning,
{ICML} 2019, 9-15 June 2019, Long Beach, California, {USA}},
 editor = {Kamalika Chaudhuri and
Ruslan Salakhutdinov},
 pages = {2170--2179},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Tue, 11 Jun 2019 01:00:00 +0200},
 title = {DeepMDP: Learning Continuous Latent Space Models for Representation
Learning},
 url = {http://proceedings.mlr.press/v97/gelada19a.html},
 volume = {97},
 year = {2019}
}

@article{FernsBisim,
 abstract = { In recent years, various metrics have been developed for measuring the behavioral similarity of states in probabilistic transition systems [J. Desharnais et al., Proceedings of CONCUR'99, Springer-Verlag, London, 1999, pp. 258–273; F. van Breugel and J. Worrell, Proceedings of ICALP'01, Springer-Verlag, London, 2001, pp. 421–432]. In the context of finite Markov decision processes (MDPs), we have built on these metrics to provide a robust quantitative analogue of stochastic bisimulation [N. Ferns, P. Panangaden, and D. Precup, Proceedings of UAI-04, AUAI Press, Arlington, VA, 2004, pp. 162–169] and an efficient algorithm for its calculation [N. Ferns, P. Panangaden, and D. Precup, Proceedings of UAI-06, AUAI Press, Arlington, VA, 2006, pp. 174–181]. In this paper, we seek to properly extend these bisimulation metrics to MDPs with continuous state spaces. In particular, we provide the first distance-estimation scheme for metrics based on bisimulation for continuous probabilistic transition systems. Our work, based on statistical sampling and infinite dimensional linear programming, is a crucial first step in formally guiding real-world planning, where tasks are usually continuous and highly stochastic in nature, e.g., robot navigation, and often a substitution with a parametric model or crude finite approximation must be made. We show that the optimal value function associated with a discounted infinite-horizon planning task is continuous with respect to metric distances. Thus, our metrics allow one to reason about the quality of solution obtained by replacing one model with another. Alternatively, they may potentially be used directly for state aggregation. },
 author = {Ferns, Norm and Panangaden, Prakash and Precup, Doina},
 doi = {10.1137/10080484X},
 eprint = { 
https://doi.org/10.1137/10080484X

},
 journal = {SIAM Journal on Computing},
 number = {6},
 pages = {1662-1714},
 title = {Bisimulation Metrics for Continuous Markov Decision Processes},
 url = { 
https://doi.org/10.1137/10080484X

},
 volume = {40},
 year = {2011}
}

@inproceedings{PiBisim,
 author = {Pablo Samuel Castro},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/aaai/Castro20.bib},
 booktitle = {The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI}
2020, The Thirty-Second Innovative Applications of Artificial Intelligence
Conference, {IAAI} 2020, The Tenth {AAAI} Symposium on Educational
Advances in Artificial Intelligence, {EAAI} 2020, New York, NY, USA,
February 7-12, 2020},
 pages = {10069--10076},
 publisher = {{AAAI} Press},
 timestamp = {Thu, 04 Jun 2020 01:00:00 +0200},
 title = {Scalable Methods for Computing State Similarity in Deterministic Markov
Decision Processes},
 url = {https://aaai.org/ojs/index.php/AAAI/article/view/6564},
 year = {2020}
}

@inproceedings{2002.05709,
 author = {Ting Chen and
Simon Kornblith and
Mohammad Norouzi and
Geoffrey E. Hinton},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/icml/ChenK0H20.bib},
 booktitle = {Proceedings of the 37th International Conference on Machine Learning,
{ICML} 2020, 13-18 July 2020, Virtual Event},
 pages = {1597--1607},
 publisher = {{PMLR}},
 series = {Proceedings of Machine Learning Research},
 timestamp = {Tue, 15 Dec 2020 00:00:00 +0100},
 title = {A Simple Framework for Contrastive Learning of Visual Representations},
 url = {http://proceedings.mlr.press/v119/chen20j.html},
 volume = {119},
 year = {2020}
}

@inproceedings{DiversityIAYD,
 author = {Benjamin Eysenbach and
Abhishek Gupta and
Julian Ibarz and
Sergey Levine},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/iclr/EysenbachGIL19.bib},
 booktitle = {7th International Conference on Learning Representations, {ICLR} 2019,
New Orleans, LA, USA, May 6-9, 2019},
 publisher = {OpenReview.net},
 timestamp = {Thu, 25 Jul 2019 01:00:00 +0200},
 title = {Diversity is All You Need: Learning Skills without a Reward Function},
 url = {https://openreview.net/forum?id=SJx63jRqFm},
 year = {2019}
}

@inproceedings{DBC,
 author = {Amy Zhang and
Rowan Thomas McAllister and
Roberto Calandra and
Yarin Gal and
Sergey Levine},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/iclr/0001MCGL21.bib},
 booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
Virtual Event, Austria, May 3-7, 2021},
 publisher = {OpenReview.net},
 timestamp = {Wed, 23 Jun 2021 01:00:00 +0200},
 title = {Learning Invariant Representations for Reinforcement Learning without
Reconstruction},
 url = {https://openreview.net/forum?id=-2FCwDKRREu},
 year = {2021}
}

@inproceedings{CausalConfusion,
 author = {Pim de Haan and
Dinesh Jayaraman and
Sergey Levine},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/nips/HaanJL19.bib},
 booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
on Neural Information Processing Systems 2019, NeurIPS 2019, December
8-14, 2019, Vancouver, BC, Canada},
 editor = {Hanna M. Wallach and
Hugo Larochelle and
Alina Beygelzimer and
Florence d'Alch{\'{e}}{-}Buc and
Emily B. Fox and
Roman Garnett},
 pages = {11693--11704},
 timestamp = {Thu, 21 Jan 2021 00:00:00 +0100},
 title = {Causal Confusion in Imitation Learning},
 url = {https://proceedings.neurips.cc/paper/2019/hash/947018640bf36a2bb609d3557a285329-Abstract.html},
 year = {2019}
}

@misc{RPC,
 author = {Benjamin Eysenbach and Ruslan Salakhutdinov and Sergey Levine},
 journal = {ArXiv preprint},
 title = {Robust Predictable Control},
 url = {https://arxiv.org/abs/2109.03214},
 volume = {abs/2109.03214},
 year = {2021}
}

@inproceedings{DVIB,
 author = {Alexander A. Alemi and
Ian Fischer and
Joshua V. Dillon and
Kevin Murphy},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/conf/iclr/AlemiFD017.bib},
 booktitle = {5th International Conference on Learning Representations, {ICLR} 2017,
Toulon, France, April 24-26, 2017, Conference Track Proceedings},
 publisher = {OpenReview.net},
 timestamp = {Thu, 25 Jul 2019 01:00:00 +0200},
 title = {Deep Variational Information Bottleneck},
 url = {https://openreview.net/forum?id=HyxQzBceg},
 year = {2017}
}

@misc{ConstrainedNetwork,
 author = {Tu-Hoa Pham and Giovanni De Magistris and Don Joven Agravante and Subhajit Chaudhury and Asim Munawar and Ryuki Tachibana},
 journal = {ArXiv preprint},
 title = {Constrained Exploration and Recovery from Experience Shaping},
 url = {https://arxiv.org/abs/1809.08925},
 volume = {abs/1809.08925},
 year = {2018}
}
