%% Journal article
@article{bib1,
  author		= "Campbell, S. L. and Gear, C. W.",
  title			= "The index of general nonlinear {D}{A}{E}{S}",
  journal		= "Numer. {M}ath.",
  volume		= "72",
  number		= "2",
  pages			= "173--196",
  year			= "1995"
}

%% Journal article with DOI
@article{bib2,
  author		= "Slifka, M. K. and Whitton, J. L.",
  title			= "Clinical implications of dysregulated cytokine production",
  journal		= "J. {M}ol. {M}ed.",
  volume		= "78",
  pages			= "74--80",
  year			= "2000",
  doi			= "10.1007/s001090000086"
}

%% Journal article
@article{bib3,
  author		= "Hamburger, C.",
  title			= "Quasimonotonicity, regularity and duality for nonlinear systems of 
					partial differential equations",
  journal		= "Ann. Mat. Pura. Appl.",
  volume		= "169",
  number		= "2",
  pages			= "321--354",
  year			= "1995"
}

%% book, authored
@book{bib4,
  author		= "Geddes, K. O. and Czapor, S. R. and Labahn, G.",
  title			= "Algorithms for {C}omputer {A}lgebra",
  address		= "Boston",
  publisher		= "Kluwer",
  year			= "1992"
}

%% Item 8. Book, chapter
@incollection{bib5,
  author		= "Broy, M.",
  title			= "Software engineering---from auxiliary to key technologies",
  editor		= "Broy, M. and Denert, E.",
  booktitle		= "Software Pioneers",
  pages			= "10--13",
  address		= "New {Y}ork",
  publisher		= "Springer",
  year			= "1992"
}

%% Book, edited
@book{bib6,
  editor		= "Seymour, R. S.",
  title			= "Conductive {P}olymers",
  address		= "New {Y}ork",
  publisher		= "Plenum",
  year			= "1981"
}

%% Chapter in a book in a series with volume titles
@inproceedings{bib7,
  author		= "Smith, S. E.",
  title			= "Neuromuscular blocking drugs in man",
  editor		= "Zaimis, E.",
  volume		= "42",
  booktitle		= "Neuromuscular junction. {H}andbook of experimental pharmacology",
  pages			= "593--660",
  address		= "Heidelberg",
  publisher		= "Springer",
  year			= "1976"
}

%% Paper presented at a conference
@misc{bib8,
  author		= "Chung, S. T. and Morris, R. L.",
  title			= "Isolation and characterization of plasmid deoxyribonucleic acid from 
					Streptomyces fradiae",
  year			= "1978",
  note			= "Paper presented at the 3rd international symposium on the genetics 
					of industrial microorganisms, University of {W}isconsin, {M}adison, 
					4--9 June 1978"
}

%% Data citation example
@misc{bib9,
  author		= "Hao, Z. and AghaKouchak, A. and Nakhjiri, N. and Farahmand, A.",
  title			= "Global integrated drought monitoring and prediction system (GIDMaPS) data sets", 
  year			= "2014",
  note			= "figshare \url{https://doi.org/10.6084/m9.figshare.853801}"
}

%% Preprint citation example
@misc{bib10, 
  author		= "Babichev, S. A. and Ries, J. and Lvovsky, A. I.",
  title			= "Quantum scissors: teleportation of single-mode optical states by means 
					of a nonlocal single photon", 
  year			= "2002",
  note			= "Preprint at \url{https://arxiv.org/abs/quant-ph/0208066v1}"
}

@article{bib11,
  author		= "Beneke, M. and Buchalla, G. and Dunietz, I.",
  title			= "Mixing induced {CP} asymmetries in inclusive {B} decays",
  journal		= "Phys. {L}ett.",
  volume		= "B393",
  year			= "1997",
  pages			= "132-142",
  archivePrefix		= "arXiv",
  eprint		= "0707.3168",
  primaryClass		= "gr-gc"
}

@softmisc{bib12,
  author		= "Stahl, B.",
  title			= "deep{SIP}: deep learning of {S}upernova {I}a {P}arameters",
  version		= "0.42",
  keywords		= "Software",
  howpublished		= "Astrophysics {S}ource {C}ode {L}ibrary",
  year			= "2020",
  month			= "Jun",
  eid			= "ascl:2006.023",
  pages			= "ascl:2006.023",
  archivePrefix		= "ascl",
  eprint		= "2006.023",
  adsurl		= "{https://ui.adsabs.harvard.edu/abs/2020ascl.soft06023S}",
  adsnote		= "Provided by the SAO/NASA Astrophysics Data System"
}

@article{bib13,
  author = "Abbott, T. M. C. and others",
  collaboration = "DES",
  title = "{Dark Energy Survey Year 1 Results: Constraints on Extended Cosmological Models from Galaxy Clustering and Weak Lensing}",
  eprint = "1810.02499",
  archivePrefix = "arXiv",
  primaryClass = "astro-ph.CO",
  reportNumber = "FERMILAB-PUB-18-507-PPD",
  doi = "10.1103/PhysRevD.99.123505",
  journal = "Phys. Rev. D",
  volume = "99",
  number = "12",
  pages = "123505",
  year = "2019"
}

%%============================================================================%%
%% while using chicago reference style, both abbreviated and expanded form of %%
%% author name format is acceptable. Refer below example for expanded form    %%
%%============================================================================%%

%%  author		= "{Cameron, Deborah}", - single author
%%  author		= "{Saito, Yukio} and {Hyuga, Hiroyuki}", - double author 

%%======================================%%
%% Example for author names with suffix %%
%%======================================%%

%%  author		= "{Price, R. A. Jr} and {Curry, N. {III}} and McCann, K. E. and 
%%					Fielding, J. L. and {Abercrombie, E. Jr}",
@article{mazyavkina2021reinforcement,
  title={Reinforcement learning for combinatorial optimization: A survey},
  author={Mazyavkina, Nina and Sviridov, Sergey and Ivanov, Sergei and Burnaev, Evgeny},
  journal={Computers \& Operations Research},
  volume={134},
  pages={105400},
  year={2021},
  publisher={Elsevier},
  issn = {0305-0548}
}


@article{GPT1,
  title={GPT-3: Its nature, scope, limits, and consequences},
  author={Floridi, Luciano and Chiriatti, Massimo},
  journal={Minds and Machines},
  volume={30},
  pages={681--694},
  year={2020},
  publisher={Springer}
}

@article{wurenji1,
  title={Champion-level drone racing using deep reinforcement learning},
  author={Kaufmann, Elia and Bauersfeld, Leonard and Loquercio, Antonio and M{\"u}ller, Matthias and Koltun, Vladlen and Scaramuzza, Davide},
  journal={Nature},
  volume={620},
  number={7976},
  pages={982--987},
  year={2023},
  publisher={Nature Publishing Group UK London}
    
}

@inproceedings{wurenji2,
  title={Autonomous drone racing with deep reinforcement learning},
  author={Song, Yunlong and Steinweg, Mats and Kaufmann, Elia and Scaramuzza, Davide},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems },
  pages={1205--1212},
  year={2021},
doi={10.1109/IROS51168.2021.9636053}}
 
}

@inproceedings{resys,
  title={Self-supervised reinforcement learning for recommender systems},
  author={Xin, Xin and Karatzoglou, Alexandros and Arapakis, Ioannis and Jose, Joemon M},
  booktitle={Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval},
  pages={931--940},
  year={2020}
}



@inproceedings{DQN,
  title={Deep reinforcement learning with double {Q}-learning},
  author={Van Hasselt, Hado and Guez, Arthur and Silver, David},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={30},
  number={1},
  pages={2094--2100},
  year={2016}
}

Export Citations

BibTeX



@ARTICLE{yangbenxiaolv1,

  author={Zhu, Zeyu and Zhao, Huijing},

  journal={IEEE Transactions on Intelligent Transportation Systems}, 

  title={A Survey of Deep {RL} and {IL} for Autonomous Driving Policy Learning}, 

  year={2022},

  volume={23},

  number={9},

  pages={14043-14065}
}

@book{maerkefu,
  title={Handbook of Markov decision processes: methods and applications},
  author={Feinberg, Eugene A and Shwartz, Adam},
  volume={40},
    pages={},
  year={2012},
  publisher={Springer Science \& Business Media}
}

@inproceedings{sample-eff,
  title={Towards sample efficient reinforcement learning},
  author={Yu, Yang},
  booktitle={Proceedings of the 27th International Joint Conference on Artificial Intelligence},
  pages={5739--5743},
  year={2018}
}

@inproceedings{yangbenxiaolv2,
  title={Feature expansive reward learning: Rethinking human input},
  author={Bobu, Andreea and Wiggert, Marius and Tomlin, Claire and Dragan, Anca D},
  booktitle={Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction},
  pages={216--224},
  year={2021}
}


@article{hirl2,
  title={Toward human-in-the-loop {AI}: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving},
  author={Wu, Jingda and Huang, Zhiyu and Hu, Zhongxu and Lv, Chen},
  journal={Engineering},
  volume={21},
  pages={75--91},
  year={2023},
  publisher={Elsevier}
}

@inproceedings{hirl3,
  title={Where to add actions in human-in-the-loop reinforcement learning},
  author={Mandel, Travis and Liu, YunEn and Brunskill, Emma and Popovi{\'c}, Zoran},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={31},
  number={1},
  year={2017}
}

@inproceedings{IA_RL,
  title={Trial without Error: Towards Safe Reinforcement Learning via Human Intervention},
  author={Saunders, William and Sastry, Girish and Stuhlm{\"u}ller, Andreas and Evans, Owain},
  booktitle={Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems},
  pages={2067--2069},
  year={2018}

}
@inproceedings{nanda_YUYANG,
  title={How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement},
  author={Liu, XuHui and Xu, Feng and Zhang, Xinyu and Liu, Tianyuan and Jiang, Shengyi and Chen, Ruifeng and Zhang, Zongzhang and Yu, Yang},
  booktitle={Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
  pages={1276--1284},
  year={2023}
   
}

@incollection{at1,
title = {Adaptive Auxiliary Task Weighting for Reinforcement Learning},
author = {Lin, Xingyu and Baweja, Harjatin and Kantor, George and Held, David},
booktitle = {Advances in Neural Information Processing Systems 32},


pages = {4772--4783},
year = {2019},


}


@article{at2,
  title={Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison},
  author={Lange, Moritz and Krystiniak, Noah and Engelhardt, Raphael C and Konen, Wolfgang and Wiskott, Laurenz},
  journal={arXiv e-prints},
  pages={arXiv--2310},
  year={2023},
    doi={https://doi.org/10.1007/978-3-031-53966-4_14}
}

@article{tsne,
  title={Visualizing Data using {t-SNE}},
  author={van der Maaten, Laurens and Hinton, Geoffrey},
  journal={Journal of Machine Learning Research},
  volume={9},
  pages={2579--2605},
  year={2008}
}

@inproceedings{at3,
  title={Auxiliary tasks benefit 3d skeleton-based human motion prediction},
  author={Xu, Chenxin and Tan, Robby T and Tan, Yuhong and Chen, Siheng and Wang, Xinchao and Wang, Yanfeng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={9509--9520},
  year={2023}
}

@inproceedings{at4,
  title={Decoupling representation learning from reinforcement learning},
  author={Stooke, Adam and Lee, Kimin and Abbeel, Pieter and Laskin, Michael},
  booktitle={International Conference on Machine Learning},
  pages={9870--9879},
  year={2021},
  organization={PMLR}
}

@inproceedings{at5curl,
  title={Curl: Contrastive unsupervised representations for reinforcement learning},
  author={Laskin, Michael and Srinivas, Aravind and Abbeel, Pieter},
  booktitle={International Conference on Machine Learning},
  pages={5639--5650},
  year={2020},
  organization={PMLR}
}

@article{at6,
  title={Adapting auxiliary losses using gradient similarity},
  author={Du, Yunshu and Czarnecki, Wojciech M and Jayakumar, Siddhant M and Farajtabar, Mehrdad and Pascanu, Razvan and Lakshminarayanan, Balaji},
  journal={arXiv preprint arXiv:1812.02224},
  year={2018},
doi ={
https://doi.org/10.48550/arXiv.1812.02224}




}

@INPROCEEDINGS{bc1,

    AUTHOR    = {Aravind Rajeswaran AND Vikash Kumar AND Abhishek Gupta AND

                 Giulia Vezzani AND John Schulman AND Emanuel Todorov AND Sergey Levine},

    TITLE     = "{Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations}",

    BOOKTITLE = {Proceedings of Robotics: Science and Systems},

    YEAR      = {2018},

}

@inproceedings{bc2,
  title={Variational imitation learning with diverse-quality demonstrations},
  author={Tangkaratt, Voot and Han, Bo and Khan, Mohammad Emtiyaz and Sugiyama, Masashi},
  booktitle={International Conference on Machine Learning},
  pages={9407--9417},
  year={2020},
  organization={PMLR}
}

@inproceedings{bc3,
  title={Deep {Q}-learning from Demonstrations},
  author={Hester, Todd and Vecerik, Matej and Pietquin, Olivier and Lanctot, Marc and Schaul, Tom and Piot, Bilal and Horgan, Dan and Quan, John and Sendonaris, Andrew and Osband, Ian and others},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={32},
  number={1},
  pages={3223--3230},
  year={2018}
}


@inproceedings{biaozheng_at,
  title={On the effect of auxiliary tasks on representation dynamics},
  author={Lyle, Clare and Rowland, Mark and Ostrovski, Georg and Dabney, Will},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1--9},
  year={2021},
  organization={PMLR}
}

@InProceedings{biaozheng2023_CVPR,
    author    = {He, Qiang and Su, Huangyuan and Zhang, Jieyu and Hou, Xinwen},
    title     = {Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    month     = {June},
    year      = {2023},
    pages     = {20215-20225},
    doi = {
https://doi.org/10.48550/arXiv.2205.14557}
}

@book{ abelson-et-al:scheme,
  author = "Harold Abelson and Gerald~Jay Sussman and Julie Sussman",
  title = "Structure and Interpretation of Computer Programs",
  publisher = "MIT Press",
  address = "Cambridge, Massachusetts",
  year = "1985"
}

@inproceedings{ bgf:Lixto,
  author = "Robert Baumgartner and Georg Gottlob and Sergio Flesca",
  title = "Visual Information Extraction with {Lixto}",
  booktitle = "Proceedings of the 27th International Conference on Very Large Databases",
  pages = "119--128",
  publisher = "Morgan Kaufmann",
  address = "Rome, Italy",
  month = "September",
  year = "2001"
}

@article{ brachman-schmolze:kl-one,
  author = "Ronald~J. Brachman and James~G. Schmolze",
  title = "An overview of the {KL-ONE} knowledge representation system",
  journal = "Cognitive Science",
  volume = "9",
  number = "2",
  pages = "171--216",
  month = "April--June",
  year = "1985"
}

@article{ gottlob:nonmon,
  author = "Georg Gottlob",
  title = "Complexity results for nonmonotonic logics",
  journal = "Journal of Logic and Computation",
  volume = "2",
  number = "3",
  pages = "397--425",
  month = "June",
  year = "1992"
}

@article{ gls:hypertrees,
  author = "Georg Gottlob and Nicola Leone and Francesco Scarcello",
  title = "Hypertree Decompositions and Tractable Queries",
  journal = "Journal of Computer and System Sciences",
  volume = "64",
  number = "3",
  pages = "579--627",
  month = "May",
  year = "2002"
}

@article{ levesque:functional-foundations,
  author = "Hector~J. Levesque",
  title = "Foundations of a functional approach to knowledge representation",
  journal = "Artificial Intelligence",
  volume = "23",
  number = "2",
  pages = "155--212",
  month = "July",
  year = "1984"
}

@inproceedings{ levesque:belief,
  author = "Hector~J. Levesque",
  title = "A logic of implicit and explicit belief",
  booktitle = "Proceedings of the Fourth National Conference on Artificial Intelligence",
  publisher = "American Association for Artificial Intelligence",
  pages = "198--202",
  address = "Austin, Texas",
  month = "August",
  year = "1984"
}

@article{ nebel:jair-2000,
  author = "Bernhard Nebel",
  title = "On the compilability and expressive power of propositional planning formalisms",
  journal = "Journal of Artificial Intelligence Research",
  volume = "12",
  pages = "271--315",
  year = "2000"
}

@inproceedings{mujoco,
  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}
}

 @misc{proceedings,
  author = {{IJCAI Proceedings}},
  title = {{IJCAI} Camera Ready Submission},
  howpublished = {\url{https://proceedings.ijcai.org/info}},
}


@article{alphago,
	author    = {David Silver and
	Aja Huang and
	Chris J. Maddison and
	Arthur Guez and
	Laurent Sifre and
	George van den Driessche and
	Julian Schrittwieser and
	Ioannis Antonoglou and
	Vedavyas Panneershelvam and
	Marc Lanctot and
	Sander Dieleman and
	Dominik Grewe and
	John Nham and
	Nal Kalchbrenner and
	Ilya Sutskever and
	Timothy P. Lillicrap and
	Madeleine Leach and
	Koray Kavukcuoglu and
	Thore Graepel and
	Demis Hassabis},
	title     = {Mastering the game of Go with deep neural networks and tree search},
	journal   = {Nature},
	volume    = {529},
	number    = {7587},
	pages     = {484--489},
	year      = {2016},
	

	timestamp = {Mon, 27 Sep 2021 17:38:59 +0200}
}

@inproceedings{TD3,
	author    = {Scott Fujimoto and
	Herke van Hoof and
	David Meger},

	title     = {Addressing Function Approximation Error in Actor-Critic Methods},
	booktitle = { International Conference on Machine Learning
	 },
	
	pages     = {1582--1591},

	year      = {2018},
	timestamp = {Wed, 03 Apr 2019 18:17:30 +0200},
	biburl    = {https://dblp.org/rec/conf/icml/FujimotoHM18.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{IFgongyeZHENG2023119684,
title = {Safe reinforcement learning for industrial optimal control: A case study from metallurgical industry},
journal = {Information Sciences},
volume = {649},
pages = {119684},
year = {2023},
issn = {0020-0255},
author = {Jun Zheng and Runda Jia and Shaoning Liu and Dakuo He and Kang Li and Fuli Wang},
keywords = {Augmented Lagrangian, Control barrier function, Gold cyanide leaching process, Industrial optimal control, Safe reinforcement learning},
abstract = {Gold cyanide leaching is a critical step in the extraction of gold from ore. The desire for a higher leaching rate often leads to increased cyanide concentrations, which pose safety risks and raise the cost of waste treatment. To address this problem, this study introduces a novel safe reinforcement learning algorithm that satisfies joint chance constraints with a high probability for multi-constraint gold cyanide leaching processes. In particular, the proposed algorithm employs chance control barrier functions to maintain the state within the desired safe set with high probability and transforms the joint chance constraint into a cumulative cost form using a constraint relaxation method. This relaxation method guarantees the satisfaction of safety requirements within a specified time horizon. A surrogate objective function optimized by stochastic gradient ascent is derived to ensure monotonic improvement of the policy in the trust region. The augmented Lagrangian-based constrained policy optimization is utilized, converting the constrained optimization problem into an unconstrained saddle-point optimization problem and avoiding the periodic performance oscillations common in the general Lagrangian method. Case studies demonstrate that the proposed algorithm outperforms baseline algorithms in terms of policy improvement, and constraint satisfaction and operates safely in multi-constraint scenarios.}
}
@article{IFGONGYEDENG2023221,
title = {Offline reinforcement learning for industrial process control: A case study from steel industry},
journal = {Information Sciences},
volume = {632},
pages = {221-231},
year = {2023},
issn = {0020-0255},
author = {Jifei Deng and Seppo Sierla and Jie Sun and Valeriy Vyatkin},
keywords = {Offline reinforcement learning, Deep ensemble, Industrial process control, Steel industry, Strip rolling},
abstract = {Flatness is a crucial indicator of strip quality that presents a challenge in regulation due to the high-speed process and the nonlinear relationship between flatness and process parameters. Conventional methods for controlling flatness are based on the first principles, empirical models, and predesigned rules, which are less adaptable to changing rolling conditions. To address this limitation, this paper proposed an offline reinforcement learning (RL) based data-driven method for flatness control. Based on the data collected from a factory, the offline RL method can learn the process dynamics from data to generate a control policy. Unlike online RL methods, the proposed method does not require a simulator for training, the policy can be potentially safer and more accurate since a simulator involves simplifications that can introduce bias. To obtain a steady performance, the proposed method incorporated ensemble Q-functions into policy evaluation to address uncertainty estimation. To address distributional shifts, based on Q-values from ensemble Q-functions, behavior cloning was added to policy improvement. Simulation and comparison results showed that the proposed method outperformed the state-of-the-art offline RL methods and achieved the best performance in producing strips with lower flatness.}
}
@article{IFhumanPERRUSQUIA2021358,
title = {Nonlinear control using human behavior learning},
journal = {Information Sciences},
volume = {569},
pages = {358-375},
year = {2021},
issn = {0020-0255},
author = {Adolfo Perrusquía and Wen Yu and Xiaoou Li},
keywords = {Nonlinear control, Reinforcement learning, Human behavior learning, Cognitive model, Skills, Emotions},
abstract = {In this paper, we discuss a human behavior learning approach for nonlinear systems control. We use several cognitive models and human skills to model and accelerate the learning process. A neural reinforcement learning algorithm is applied as main cognitive model. A persistent exciting signal and experience replay methods are proposed to improve learning accuracy and overcome the sensitivity problem of the human actions. The stability and convergence of the neural network based reinforcement learning is discussed. Simulations results verify the approach with two benchmark control problems.}
}
@article{IFMTYIN2024120362,
title = {MAInt: A multi-task learning model with automatic feature interaction learning for personalized recommendations},
journal = {Information Sciences},
volume = {665},
pages = {120362},
year = {2024},
issn = {0020-0255},
author = {Pu Yin and Yetao Sun and Ziyi Gao and Rui Wang and Yuan Yao},
keywords = {Multi-task learning, Recommender systems, Feature interaction learning, Self-attention},
abstract = {Nowadays, more and more recommender systems apply multi-task learning to provide users with more accurate personalized recommendations. However, most of the existing multi-task learning models ignore the importance of explicit feature interactions in different tasks, which are an important part of recommender system models. In addition, the knowledge sharing mechanism of the existing model is difficult to meet the needs of all tasks, resulting in the performance degradation of some tasks. To address these issues, we propose a Multi-task learning model based on Automatic feature Interaction learning (MAInt). The Tasks Interaction Layer in MAInt automatically learns explicit feature interactions in different tasks through self-attention neural networks. At the same time, this process allows each task to interact with each other, so that each task can adaptively extract helpful information from other task features. We conducted multiple experiments on two popular public datasets: Census-income Dataset and Ali-CCP Dataset, and the experimental results verified the effectiveness of our proposed model.}
}
//42
@article{IFROBOTFERNANDEZGAUNA2013309,
title = {Undesired state-action prediction in multi-agent reinforcement learning for linked multi-component robotic system control},
journal = {Information Sciences},
volume = {232},
pages = {309-324},
year = {2013},
issn = {0020-0255},
author = {Borja Fernandez-Gauna and Ion Marques and Manuel Graña},
keywords = {Hose transportation, Multi-robot systems, Multi-agent reinforcement learning, Distributed Q-Learning},
abstract = {The paper deals with the problem of learning the control of Multi-Component Robotic Systems (MCRSs) applying Multi-Agent Reinforcement Learning (MARL) algorithms. Modeling Linked MCRS usually leads to over-constrained environments, posing great difficulties for efficient learning with conventional single and multi-agent reinforcement algorithms. In this paper, we propose a hybrid learning algorithm composed of a modified Q-Learning algorithm embedding an Undesired State-Action Prediction (USAP) module trained by a supervised learning approach which learns a model predicting undesired transitions to states breaking physical constraints. The USAP module’s output is used by the Q-Learning algorithm to prevent these undesired transitions, therefore boosting learning efficiency. This hybrid approach is extended to the multi-agent case embedding the USAP module in Distributed Round-Robin Q-Learning (D-RR-QL), which requires very little communications among agents. We present results of computational experiments conducted in the classical multi-agent taxi scheduling task and a hose transportation task. Results show a considerable learning gain both in time and accuracy, compared to the state-of-the-art Distributed Q-Learning approach in the deterministic taxi scheduling task. In the hose transportation task, USAP module introduces a significant improvement in learning convergence speed.}
}

@article{IFrobotYAO2022317,
title = {Graph and dynamics interpretation in robotic reinforcement learning task},
journal = {Information Sciences},
volume = {611},
pages = {317-334},
year = {2022},
issn = {0020-0255},
author = {Zonggui Yao and Jun Yu and Jian Zhang and Wei He},
keywords = {Graph neural networks (GNNs), Dynamics estimations, Robotic controls, Robotic force transmission, Trajectory following, Reinforcement learning},
abstract = {Robot control tasks are typically solved by reinforcement learning approaches in a circular way of trial and learn. A recent trend of the research on robotic reinforcement learning is the employment of the deep learning methods. Existing deep learning methods achieve the control by training the approximation models of the dynamic function, value function or the policy function in the control algorithms. However, these methods usually handle the modeling from a statistical perspective without considering the physics characteristics of the robot’s motion. One of the typical problems is the force transmission through different parts of a robot and the calculations of the robotic dynamics quantities are prone to be ignored. To this end, we propose to use the force transmission graph to interpret the force transmission mechanism obeyed by the motion of the robot and estimate the dynamics quantities of the robot’s motion with a quadratic model. Following this concern, we propose a model-based reinforcement learning framework for robotic control in which the dynamic model comprises two components, i.e. the Graph Convolution Network (GCN) and the Two-Layer Perception (TLP) network. The GCN serves as a parameter estimator of the force transmission graph and a structural feature extractor. The TLP network approximates the quadratic model that should be able to estimate the dynamics quantities of the robot’s motion. For this reason, the proposed framework is named as GCN of Dynamics estimation in Reinforcement Learning method (GDRL for short). The deployed method interprets the intrinsic mechanism of robotic force transmissions through robot limbs, therefore the model is highly interpretable. Experimental results show that GDRL can predict the gesture and location of the robot for the next move well such that the performance of our method surpasses that of the previous methods in robot control task in our task setting of multiple types of robots. We also designed to compare with the previous model-free methods in our task setting, and the results are outstanding, which is owing to the interpretation of the physical characteristics.}
}
@article{ppo,
	author    = {John Schulman and
	Filip Wolski and
	Prafulla Dhariwal and
	Alec Radford and
	Oleg Klimov},
	title     = {Proximal Policy Optimization Algorithms},
	journal   = {CoRR},
	volume    = {abs/1707.06347},
	year      = {2017},
	url       = {http://arxiv.org/abs/1707.06347},
	eprinttype = {arXiv},
	eprint    = {1707.06347},
	timestamp = {Mon, 13 Aug 2018 16:47:34 +0200},
	biburl    = {https://dblp.org/rec/journals/corr/SchulmanWDRK17.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{MarkovDecisionProcesses,
title={Markov decision processes},
author={Puterman, Martin L},
journal={Handbooks in Operations Research and Management Science},
volume={2},
pages={331--434},
year={1990},
publisher={Elsevier}
}

@inproceedings{Teachingonabudget,
	author    = {Lisa Torrey and
	Matthew E. Taylor},
	title     = {Teaching on a budget: agents advising agents in reinforcement learning},
	booktitle = {International conference on Autonomous Agents and Multi-Agent Systems,
	{AAMAS} },
	pages     = {1053--1060},

	year      = {2013},
	url       = {http://dl.acm.org/citation.cfm?id=2485086},
	timestamp = {Thu, 09 Sep 2021 16:09:54 +0200},
	biburl    = {https://dblp.org/rec/conf/atal/TorreyT13.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{WeakHumanPreference,
	author    = {Zehong Cao and
	Kaichiu Wong and
	Chin{-}Teng Lin},
	title     = {Weak Human Preference Supervision for Deep Reinforcement Learning},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
	volume    = {32},
	number    = {12},
	pages     = {5369--5378},
	year      = {2021},
	timestamp = {Wed, 15 Dec 2021 10:29:37 +0100},
	
	
}

@inproceedings{oppo,
author = {Kang, Yachen and Shi, Diyuan and Liu, Jinxin and He, Li and Wang, Donglin},
title = {Beyond reward: offline preference-guided policy optimization},
year = {2023},
publisher = {JMLR.org},
abstract = {This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online interaction or specification of reward functions. Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the dynamics and task information, respectively. Since the dynamics and task information are orthogonal, a naive approach would involve using preference-based reward learning followed by an off-the-shelf offline RL algorithm. However, this requires the separate learning of a scalar reward function, which is assumed to be an information bottleneck of the learning process. To address this issue, we propose the offline preference-guided policy optimization (OPPO) paradigm, which models offline trajectories and preferences in a one-step process, eliminating the need for separately learning a reward function. OPPO achieves this by introducing an offline hindsight information matching objective for optimizing a contextual policy and a preference modeling objective for finding the optimal context. OPPO further integrates a well-performing decision policy by optimizing the two objectives iteratively. Our empirical results demonstrate that OPPO effectively models offline preferences and outperforms prior competing baselines, including offline RL algorithms performed over either true or pseudo reward function specifications. Our code is available on the project website: https://sites.google.com/view/oppo-icml-2023.},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
articleno = {645},
numpages = {16},
location = {, Honolulu, Hawaii, USA, },
series = {ICML'23},
doi ={https://dl.acm.org/doi/abs/10.5555/3618408.3619053}
}


@inproceedings{

DT,

title={Preference {T}ransformer: Modeling Human Preferences using Transformers for {RL}},

author={Changyeon Kim and Jongjin Park and Jinwoo Shin and Honglak Lee and Pieter Abbeel and Kimin Lee},

booktitle={International Conference on Learning Representations},

year={2023},



}

@inproceedings{HumanPreferences,
	author    = {Paul F. Christiano and
	Jan Leike and
	Tom B. Brown and
	Miljan Martic and
	Shane Legg and
	Dario Amodei},

	title     = {Deep Reinforcement Learning from Human Preferences},
	booktitle = {Neural Information Processing Systems},
	pages     = {4299--4307},
	year      = {2017},
	timestamp = {Thu, 21 Jan 2021 15:15:21 +0100},
}

@inproceedings{HumanDemonstrations,
	author    = {Malayandi Palan and
	Gleb Shevchuk and
	Nicholas Charles Landolfi and
	Dorsa Sadigh},

	title     = {Learning reward functions by integrating human demonstrations and
	preferences},
	booktitle = {Robotics: Science and Systems, RSS},
	year      = {2019},
	pages     = {299--307}
}

@inproceedings{lossreward,
  author={Evan Shelhamer and Parsa Mahmoudieh and Max Argus and Trevor Darrell},
  title={Loss is its own Reward: Self-Supervision for Reinforcement Learning},
  year={2017},
  cdate={1483228800000},
  
  booktitle={International Conference on
Learning Representations (Workshop)},
  crossref={conf/iclr/2017w},

}


@inproceedings{FeedbackEfficient ,
	author    = {Kimin Lee and
	Laura M. Smith and
	Pieter Abbeel},
	title     = {{PEBBLE:} Feedback-Efficient Interactive Reinforcement Learning via
	Relabeling Experience and Unsupervised Pre-training},
	booktitle = { International Conference on Machine Learning,
	{ICML} },
	volume    = {139},
	pages     = {6152--6163},

	year      = {2021},
	url       = {http://proceedings.mlr.press/v139/lee21i.html},
	timestamp = {Wed, 25 Aug 2021 17:11:17 +0200},
	biburl    = {https://dblp.org/rec/conf/icml/LeeSA21.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{matarese2021toward,
	title={Toward Robots’ Behavioral Transparency of Temporal Difference Reinforcement Learning With a Human Teacher},
	author={Matarese, Marco and Sciutti, Alessandra and Rea, Francesco and Rossi, Silvia},
	journal={IEEE Transactions on Human-Machine Systems},
	volume={51},
	number={6},
	pages={578--589},
	year={2021},
	publisher={IEEE}
}
@inproceedings{HumanGazeAssisted,
	author    = {Ruohan Zhang and
	Akanksha Saran and
	Bo Liu and
	Yifeng Zhu and
	Sihang Guo and
	Scott Niekum and
	Dana H. Ballard and
	Mary M. Hayhoe},
	title     = {Human Gaze Assisted Artificial Intelligence: {A} Review},
	booktitle = { International Joint Conference on
	Artificial Intelligence, {IJCAI} },
	pages     = {4951--4958},
	publisher = {{PMLR}},
	year      = {2020},
	url       = {https://doi.org/10.24963/ijcai.2020/689},
	doi       = {10.24963/ijcai.2020/689},
	timestamp = {Fri, 25 Dec 2020 01:15:12 +0100},
	biburl    = {https://dblp.org/rec/conf/ijcai/ZhangSLZGNBH20.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}


//21

@article{tamer2012,
title={How humans teach agents},
author={Knox, W Bradley and Glass, Brian D and Love, Bradley C and Maddox, W Todd and Stone, Peter},
journal={International Journal of Social Robotics},
volume={4},
number={4},
pages={409--421},
year={2012},
publisher={Springer}
}


@inproceedings{tamer2013,
	author    = {W. Bradley Knox and
	Peter Stone},

	title     = {Learning non-myopically from human-generated reward},
	booktitle = { International Conference on Intelligent User Interfaces, {IUI}},
	pages     = {191--202},

	year      = {2013},
	url       = {https://doi.org/10.1145/2449396.2449422},
	doi       = {10.1145/2449396.2449422},
	timestamp = {Tue, 06 Nov 2018 11:07:40 +0100},
	biburl    = {https://dblp.org/rec/conf/iui/KnoxS13.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{tamper,
	author    = {Riku Arakawa and
	Sosuke Kobayashi and
	Yuya Unno and
	Yuta Tsuboi and
	Shin{-}ichi Maeda},
	title     = {{DQN-TAMER:} Human-in-the-Loop Reinforcement Learning with Intractable
	Feedback},


	year      = {2018},
	
	journal   = {abs/1810.11748},
	note = {[Online].  Available: {http://arxiv.org/abs/1810.11748}},
	
	url       = {http://arxiv.org/abs/1810.11748},
	eprinttype = {arXiv},
	eprint    = {1810.11748},
	timestamp = {Wed, 31 Oct 2018 14:24:29 +0100},
	biburl    = {https://dblp.org/rec/journals/corr/abs-1810-11748.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{AT1.11,
  title={Cross-lingual language model pretraining},
  author={Conneau, Alexis and Lample, Guillaume},
  booktitle={Proceedings of the 33rd International Conference on Neural Information Processing Systems},
  year={2019},
  pages = {7059--7069},
  article={634},
}

@article{zhouyuhao,
  author={Zhou, Yuhao and Luo, Biao and Wang, Xin and Xu, Xiaodong and Xiao, Lin},
  journal={IEEE Transactions on Circuits and Systems I: Regular Papers}, 
  title={R{L}-Based Adaptive Optimal Bipartite Consensus Control for Nonlinear Heterogeneous MASs via Event-Triggered State Feedback}, 
  year={2024},
  volume={71},
  number={9},
  pages={4261-4273},
  keywords={Consensus control;Optimal control;Artificial neural networks;Aerodynamics;Multi-agent systems;Approximation algorithms;System dynamics;Bipartite consensus;reinforcement learning;heterogeneous multiagent systems;event-triggered state feedback;neural networks}
  }

@article{luo2025survey,
  author = {Luo, Biao and Hu, Tian-Meng and Zhou, Yu-Hao and Huang, Ting-Wen and Yang, Chun-Hua and Gui, Wei-Hua},
  title = {Survey on multi-agent reinforcement learning for control and decision-making},
  journal = {Acta Automatica Sinica},
  volume = {51},
  number = {3},
  pages = {510--539},
  year = {2025}
}


@inproceedings{hirl1,
	author    = {Garrett Warnell and
	Nicholas R. Waytowich and
	Vernon Lawhern and
	Peter Stone},
	title     = {Deep {TAMER:} Interactive Agent Shaping in High-Dimensional State
	Spaces},
	booktitle = {Proceedings of the AAAI conference on artificial intelligence
	},
	pages     = {1545--1554},

	year      = {2018},

	timestamp = {Tue, 08 Mar 2022 21:46:35 +0100},
	biburl    = {https://dblp.org/rec/conf/aaai/WarnellWLS18.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{Luo2024Human,
  author={Luo, Biao and Wu, Zhengke and Zhou, Fei and Wang, Bing-Chuan},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={Human-in-the-Loop Reinforcement Learning in Continuous-Action Space}, 
  year={2024},
  volume={35},
  number={11},
  pages={15735-15744}
}

@ARTICLE{Hu2023MO-MIX,
  author={Hu, Tianmeng and Luo, Biao and Yang, Chunhua and Huang, Tingwen},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={{MO-MIX}: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning}, 
  year={2023},
  volume={45},
  number={10},
  pages={12098-12112}
}

@ARTICLE{Zhou2024SMONAC,
  author={Zhou, Fei and Luo, Biao and Wu, Zhengke and Huang, Tingwen},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={{SMONAC}: Supervised Multiobjective Negative Actor–Critic for Sequential Recommendation}, 
  year={2024},
  volume={35},
  number={12},
  pages={18525-18537}
}

@ARTICLE{Ou2025Reinforcement,
  author={Ou, Wen and Luo, Biao and Xu, Xiaodong and Feng, Yu and Zhao, Yuqian},
  journal={IEEE Transactions on Artificial Intelligence}, 
  title={Reinforcement Learned Multiagent Cooperative Navigation in Hybrid Environment With Relational Graph Learning}, 
  year={2025},
  volume={6},
  number={1},
  pages={25-36}
}

@book{sutton1998reinforcement,
	Address = {Cambridge},
	Author = {Sutton, Richard S and Barto, Andrew G},
	Date-Modified = {2015-08-10 06:27:54 +0000},
	Publisher = {The MIT Press},
	Title = {Reinforcement Learning: An Introduction},
	Year = {1998}}

@article{qqq,
  title={Human-in-the-loop reinforcement learning in continuous-action space},
  author={Luo, Biao and Wu, Zhengke and Zhou, Fei and Wang, Bing-Chuan},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2023},
  publisher={IEEE},

}

@misc{TD7,
      title={For SALE: State-Action Representation Learning for Deep Reinforcement Learning}, 
      author={Scott Fujimoto and Wei-Di Chang and Edward J. Smith and Shixiang Shane Gu and Doina Precup and David Meger},
      year={2023},
      eprint={2306.02451},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{SAC,
  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{9448304,

  author={Cao, Zehong and Wong, KaiChiu and Lin, Chin-Teng},

  journal={IEEE Transactions on Neural Networks and Learning Systems}, 

  title={Weak Human Preference Supervision for Deep Reinforcement Learning}, 

  year={2021},

  volume={32},

  number={12},

  pages={5369-5378},

  doi={10.1109/TNNLS.2021.3084198}}

@inproceedings{jaderberg2016reinforcement,
  title={Reinforcement Learning with Unsupervised Auxiliary Tasks},
  author={Jaderberg, Max and Mnih, Volodymyr and Czarnecki, Wojciech Marian and Schaul, Tom and Leibo, Joel Z and Silver, David and Kavukcuoglu, Koray},
  booktitle={International Conference on Learning Representations},
  year={2016}
}

@inproceedings{NEURIPS2021_e77910eb,
 author = {Fifty, Chris and Amid, Ehsan and Zhao, Zhe and Yu, Tianhe and Anil, Rohan and Finn, Chelsea},
 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 = {27503--27516},
 publisher = {Curran Associates, Inc.},
 title = {Efficiently Identifying Task Groupings for Multi-Task Learning},
 volume = {34},
 year = {2021}
}

@article{zeng2020NeuroBayesSLAMNeurobiologically,
  title = {{{NeuroBayesSLAM}}: {{Neurobiologically}} Inspired {{Bayesian}} Integration of Multisensory Information for Robot Navigation},
  author = {Zeng, Taiping and Tang, Fengzhen and Ji, Daxiong and Si, Bailu},
  year = {2020},
  journal = {Neural Networks},
  volume = {126},
  pages = {21--35},
  issn = {0893-6080}
  
}

@inproceedings{fujimoto2018AddressingFunction,
  title = {Addressing Function Approximation Error in Actor-Critic Methods},
  booktitle = {Proceedings of the 2018 {{International Conference}} on {{Machine Learning}} ({{ICML}})},
  author = {Fujimoto, Scott and {van Hoof}, Herke and Meger, David},
  year = {2018},
  pages = {1587--1596},
}

@article{wu2022AchievingRealtime,
  title = {Achieving Real-Time Path Planning in Unknown Environments through Deep Neural Networks},
  author = {Wu, Keyu and Wang, Han and Esfahani, Mahdi Abolfazli and Yuan, Shenghai},
  year = {2022},
  month = mar,
  journal = {IEEE Transactions on Intelligent Transportation Systems},
  volume = {23},
  number = {3},
  pages = {2093--2102},
  doi = {10.1109/TITS.2020.3031962}
}

@inproceedings{liang2020NoMap,
  title = {No Map, No Problem: {{A}} Local Sensing Approach for Navigation in Human-Made Spaces Using Signs},
  booktitle = {Proceedings of the 2020 {{IEEE}}/{{RSJ}} International Conference on Intelligent Robots and Systems ({{IROS}})},
  author = {Liang, Claire and Knepper, Ross A. and Pokorny, Florian T.},
  year = {2020},
  pages = {6148--6155},
  doi = {10.1109/IROS45743.2020.9340813}
}

@article{juang2022NavigationFuzzycontrolled,
  title = {Navigation of a Fuzzy-Controlled Wheeled Robot through the Combination of Expert Knowledge and Data-Driven Multiobjective Evolutionary Learning},
  author = {Juang, Chia-Feng and Chou, Ching-Yu and Lin, Chin-Teng},
  year = {2022},
  month = aug,
  journal = {IEEE Transactions on Cybernetics},
  volume = {52},
  number = {8},
  pages = {7388--7401},
  doi = {10.1109/TCYB.2020.3041269}
}

@article{wan2020RobotassistedPedestrian,
  title = {Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning},
  author = {Wan, Zhiqiang and Jiang, Chao and Fahad, Muhammad and Ni, Zhen and Guo, Yi and He, Haibo},
  year = {2020},
  month = apr,
  journal = {IEEE Transactions on Cybernetics},
  volume = {50},
  number = {4},
  pages = {1669--1682},
  doi = {10.1109/TCYB.2018.2878977}
}

@article{qureshi2018IntrinsicallyMotivated,
  title = {Intrinsically Motivated Reinforcement Learning for Human\textendash Robot Interaction in the Real-World},
  author = {Qureshi, Ahmed Hussain and Nakamura, Yutaka and Yoshikawa, Yuichiro and Ishiguro, Hiroshi},
  year = {2018},
  journal = {Neural Networks},
  volume = {107},
  pages = {23--33},
  issn = {0893-6080}

}

@article{schilling2021DecentralizedControl,
  title = {Decentralized Control and Local Information for Robust and Adaptive Decentralized {{Deep Reinforcement Learning}}},
  author = {Schilling, Malte and Melnik, Andrew and Ohl, Frank W. and Ritter, Helge J. and Hammer, Barbara},
  year = {2021},
  journal = {Neural Networks},
  volume = {144},
  pages = {699--725},
  issn = {0893-6080}
  
}

@article{xiang2021TaskorientedDeep,
  title = {Task-Oriented Deep Reinforcement Learning for Robotic Skill Acquisition and Control},
  author = {Xiang, Guofei and Su, Jianbo},
  year = {2021},
  month = feb,
  journal = {IEEE Transactions on Cybernetics},
  volume = {51},
  number = {2},
  pages = {1056--1069},
  doi = {10.1109/TCYB.2019.2949596}
}

@article{bhutta2022LoopboxMultiagent,
  title = {Loop-Box: {{Multiagent}} Direct {{SLAM}} Triggered by Single Loop Closure for Large-Scale Mapping},
  author = {Bhutta, M. Usman Maqbool and Kuse, Manohar and Fan, Rui and Liu, Yanan and Liu, Ming},
  year = {2022},
  month = jun,
  journal = {IEEE Transactions on Cybernetics},
  volume = {52},
  number = {6},
  pages = {5088--5097},
  doi = {10.1109/TCYB.2020.3027307}
}

@article{wang2022DensityawareCurriculum,
  title = {Density-Aware Curriculum Learning for Crowd Counting},
  author = {Wang, Qi and Lin, Wei and Gao, Junyu and Li, Xuelong},
  year = {2022},
  month = jun,
  journal = {IEEE Transactions on Cybernetics},
  volume = {52},
  number = {6},
  pages = {4675--4687},
  doi = {10.1109/TCYB.2020.3033428}
}

@article{alonso-mora2018CooperativeCollision,
  title = {Cooperative Collision Avoidance for Nonholonomic Robots},
  author = {{Alonso-Mora}, Javier and Beardsley, Paul and Siegwart, Roland},
  year = {2018},
  month = apr,
  journal = {IEEE Transactions on Robotics},
  volume = {34},
  number = {2},
  pages = {404--420},
  doi = {10.1109/TRO.2018.2793890}
}

@article{bacon2017OptionCriticArchitecture,
  title = {The {{Option-critic}} Architecture},
  author = {Bacon, Pierre-Luc and Harb, Jean and Precup, Doina},
  year = {2017},
  month = feb,
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {31},
  number = {1},
  issn = {2374-3468, 2159-5399}
}

@inproceedings{barth2008WhereWill,
  title = {Where Will the Oncoming Vehicle Be the next Second?},
  booktitle = {Proceedings of the 2008 {{IEEE Intelligent Vehicles Symposium}} ({{IVS}})},
  author = {Barth, Alexander and Franke, Uwe},
  year = {2008},
  pages = {1068--1073},
  issn = {1931-0587}
}

@inproceedings{bengio2009CurriculumLearning,
  title = {Curriculum Learning},
  booktitle = {Proceedings of the 2009 {{International Conference}} on {{Machine Learning}} ({{ICML}})},
  author = {Bengio, Yoshua and Louradour, J{\'e}r{\^o}me and Collobert, Ronan and Weston, Jason},
  year = {2009},
  pages = {41--48}
}

@article{berner2019DotaLarge,
  title = {Dota 2 with Large Scale Deep Reinforcement Learning},
  author = {Berner, Christopher and Brockman, Greg and Chan, Brooke and et al.},
  year = {2019},
  journal = {arXiv preprint arXiv:1912.06680},
  eprint = {1912.06680},
  eprinttype = {arxiv},
  archiveprefix = {arXiv}
}

@article{berner2019DotaLarge1,
  title = {Dota 2 with Large Scale Deep Reinforcement Learning},
  author = {Berner, Christopher and Brockman, Greg and Chan, Brooke and Cheung, Vicki and Debiak, Przemyslaw and Dennison, Christy and Farhi, David and Fischer, Quirin and Hashme, Shariq and Hesse, Christopher and J{\'o}zefowicz, Rafal and Gray, Scott and Olsson, Catherine and Pachocki, Jakub and Petrov, Michael and {de Oliveira Pinto}, Henrique Pond{\'e} and Raiman, Jonathan and Salimans, Tim and Schlatter, Jeremy and Schneider, Jonas and Sidor, Szymon and Sutskever, Ilya and Tang, Jie and Wolski, Filip and Zhang, Susan},
  year = {2019},
  journal = {arXiv preprint arXiv:1912.06680},
  eprint = {1912.06680},
  eprinttype = {arxiv},
  archiveprefix = {arXiv}
}

@article{brito2019ModelPredictive,
  title = {Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environments},
  author = {Brito, Bruno and Floor, Boaz and Ferranti, Laura and {Alonso-Mora}, Javier},
  year = {2019},
  month = oct,
  journal = {IEEE Robotics and Automation Letters},
  volume = {4},
  number = {4},
  pages = {4459--4466},
  doi = {10.1109/LRA.2019.2929976}
}

@article{christen2021LearningFunctionally,
  title = {Learning Functionally Decomposed Hierarchies for Continuous Control Tasks with Path Planning},
  author = {Christen, Sammy and Jendele, Lukas and Aksan, Emre and Hilliges, Otmar},
  year = {2021},
  month = apr,
  journal = {IEEE Robotics and Automation Letters},
  volume = {6},
  number = {2},
  pages = {3623--3630},
  issn = {2377-3766, 2377-3774},
  doi = {10.1109/LRA.2021.3060403}
}

@inproceedings{chung2010MobileRobot,
  title = {A Mobile Robot That Understands Pedestrian Spatial Behaviors},
  booktitle = {Proceedings of the 2010 {{IEEE}}/{{RSJ International Conference}} on {{Intelligent Robots}} and {{Systems}} ({{IROS}})},
  author = {Chung, Shu-Yun and Huang, Han-Pang},
  year = {2010},
  pages = {5861--5866},
  issn = {2153-0866},
  doi = {10.1109/IROS.2010.5649718}
}

@article{devo2020GeneralizationTargetDriven,
  title = {Towards Generalization in Target-Driven Visual Navigation by Using Deep Reinforcement Learning},
  author = {Devo, Alessandro and Mezzetti, Giacomo and Costante, Gabriele and Fravolini, Mario L. and Valigi, Paolo},
  year = {2020},
  month = oct,
  journal = {IEEE Transactions on Robotics},
  volume = {36},
  number = {5},
  pages = {1546--1561},
  issn = {1941-0468},
  doi = {10.1109/TRO.2020.2994002}
}

@inproceedings{di2020LocalPath,
  title = {Local Path Planning of Mobile Robot Based on Artificial Potential Field},
  booktitle = {Proceedings of the 2020 Chinese Control Conference ({{CCC}})},
  author = {Di, Wang and Caihong, Li and Na, Guo and Yong, Song and Tengteng, Gao and Guoming, Liu},
  year = {2020},
  pages = {3677--3682},
  doi = {10.23919/CCC50068.2020.9189250}
}

@inproceedings{elnagar2001PredictionMoving,
  title = {Prediction of Moving Objects in Dynamic Environments Using {{Kalman}} Filters},
  booktitle = {Proceedings of the 2001 {{IEEE International Symposium}} on {{Computational Intelligence}} in {{Robotics}} and {{Automation}} ({{CIRA}})},
  author = {Elnagar, A.},
  year = {2001},
  pages = {414--419},
  doi = {10.1109/CIRA.2001.1013236}
}

@inproceedings{engel2014LSDSLAM,
  title = {{{LSD-SLAM}}: {{Large-scale}} Direct Monocular {{SLAM}}},
  booktitle = {Proceedings of the 2014 {{European}} Conference on Computer Vision ({{ECCV}})},
  author = {Engel, Jakob and Sch{\"o}ps, Thomas and Cremers, Daniel},
  year = {2014},
  pages = {834--849}
}

@article{fox1997DynamicWindow,
  title = {The Dynamic Window Approach to Collision Avoidance},
  author = {Fox, D. and Burgard, W. and Thrun, S.},
  year = {1997},
  month = mar,
  journal = {IEEE Robotics \& Automation Magazine},
  volume = {4},
  number = {1},
  pages = {23--33},
  doi = {10.1109/100.580977}
}

@article{fuentes-pacheco2015VisualSimultaneous,
  title = {Visual Simultaneous Localization and Mapping: A Survey},
  shorttitle = {Visual Simultaneous Localization and Mapping},
  author = {{Fuentes-Pacheco}, Jorge and {Ruiz-Ascencio}, Jos{\'e} and {Rend{\'o}n-Mancha}, Juan Manuel},
  year = {2015},
  month = jan,
  journal = {Artificial Intelligence Review},
  volume = {43},
  number = {1},
  pages = {55--81},
  issn = {1573-7462},
  doi = {10.1007/s10462-012-9365-8}
}

@inproceedings{gu2017DeepReinforcement,
  title = {Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates},
  booktitle = {Proceedings of the 2017 {{IEEE International Conference}} on {{Robotics}} and {{Automation}} ({{ICRA}})},
  author = {Gu, Shixiang and Holly, Ethan and Lillicrap, Timothy and Levine, Sergey},
  year = {2017},
  pages = {3389--3396},
  doi = {10.1109/ICRA.2017.7989385}
}

@inproceedings{gupta2018SocialGAN,
  title = {Social {{GAN}}: {{Socially}} Acceptable Trajectories with Generative Adversarial Networks},
  shorttitle = {Social {{GAN}}},
  booktitle = {Proceedings of the 2018 {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  author = {Gupta, Agrim and Johnson, Justin and {Fei-Fei}, Li and Savarese, Silvio and Alahi, Alexandre},
  year = {2018},
  pages = {2255--2264},
  doi = {10.1109/CVPR.2018.00240},
  isbn = {978-1-5386-6420-9}
}

@article{kalman1960NewApproach,
  title = {A {{New Approach}} to {{Linear Filtering}} and {{Prediction Problems}}},
  author = {Kalman, R. E.},
  year = {1960},
  month = mar,
  journal = {Journal of Basic Engineering},
  volume = {82},
  number = {1},
  pages = {35--45},
  issn = {0021-9223},
  doi = {10.1115/1.3662552},
  annotation = {\_eprint: https://asmedigitalcollection.asme.org/fluidsengineering/article-pdf/82/1/35/5518977/35\_1.pdf}
}

@article{kolve2017AI2THORInteractive,
  title = {{{AI2-THOR}}: {{An}} Interactive 3d Environment for Visual Ai},
  author = {Kolve, Eric and Mottaghi, Roozbeh and Han, Winson and VanderBilt, Eli and Weihs, Luca and Herrasti, Alvaro and Gordon, Daniel and Zhu, Yuke and Gupta, Abhinav and Farhadi, Ali},
  year = {2017},
  journal = {arXiv preprint arXiv:1712.05474},
  eprint = {1712.05474},
  eprinttype = {arxiv},
  archiveprefix = {arXiv}
}

@article{kulhánek2021VisualNavigation,
  title = {Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning},
  author = {Kulh{\'a}nek, Jon{\'a}{\v s} and Derner, Erik and Babu{\v s}ka, Robert},
  year = {2021},
  month = jul,
  journal = {IEEE Robotics and Automation Letters},
  volume = {6},
  number = {3},
  pages = {4345--4352},
  publisher = {{IEEE}}
}

@inproceedings{kulkarni2016HierarchicalDeep,
  title = {Hierarchical Deep Reinforcement Learning: {{Integrating}} Temporal Abstraction and Intrinsic Motivation},
  booktitle = {Proceedings of the 2016 {{Neural Information Processing Systems}} ({{NeurIPS}})},
  author = {Kulkarni, Tejas D. and Narasimhan, Karthik and Saeedi, Ardavan and Tenenbaum, Josh},
  year = {2016},
  pages = {3675--3683}
}

@article{lamini2018GeneticAlgorithm,
  title = {Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning},
  author = {Lamini, Chaymaa and Benhlima, Said and Elbekri, Ali},
  year = {2018},
  journal = {Procedia Computer Science},
  volume = {127},
  pages = {180--189},
  issn = {1877-0509}
  
}

@article{lavalle1998RapidlyexploringRandom,
  title = {Rapidly-Exploring Random Trees: {{A}} New Tool for Path Planning},
  author = {LaValle, Steven M},
  year = {1998},
  month = oct,
  journal = {Technical Report. Computer Science Department, Iowa State University (TR 98\textendash 11)}
}

@article{lerner2007CrowdsExample,
  title = {Crowds by Example},
  author = {Lerner, Alon and Chrysanthou, Yiorgos and Lischinski, Dani},
  year = {2007},
  journal = {Computer Graphics Forum},
  volume = {26},
  number = {3},
  pages = {655--664},
  issn = {1467-8659},
  doi = {10.1111/j.1467-8659.2007.01089.x},
  annotation = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-8659.2007.01089.x}
}

@inproceedings{levy2019LearningMultiLevel,
  title = {Learning Multi-Level Hierarchies with Hindsight},
  booktitle = {Proceedings of the 2019 {{International Conference}} on {{Learning Representations}} ({{ICLR}})},
  author = {Levy, Andrew and Konidaris, George Dimitri and Jr, Robert Platt and Saenko, Kate},
  year = {2019}
}

@inproceedings{liang2019PeekingFuture,
  title = {Peeking {{Into}} the {{Future}}: {{Predicting Future Person Activities}} and {{Locations}} in {{Videos}}},
  shorttitle = {Peeking {{Into}} the {{Future}}},
  booktitle = {Proceedings of the 2019 {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}} ({{CVPR}})},
  author = {Liang, Junwei and Jiang, Lu and Niebles, Juan Carlos and Hauptmann, Alexander G. and {Fei-Fei}, Li},
  year = {2019},
  pages = {5725--5734}
}

@article{lillicrap2015continuous,
  title = {Continuous Control with Deep Reinforcement Learning},
  author = {Lillicrap, Timothy P and Hunt, Jonathan J and Pritzel, Alexander and Heess, Nicolas and Erez, Tom and Tassa, Yuval and Silver, David and Wierstra, Daan},
  year = {2015},
  journal = {arXiv preprint arXiv:1509.02971},
  eprint = {1509.02971},
  eprinttype = {arxiv},
  archiveprefix = {arXiv}
}

@article{lu2021MGRLGraph,
  title = {{{MGRL}}: {{Graph}} Neural Network Based Inference in a {{Markov}} Network with Reinforcement Learning for Visual Navigation},
  author = {Lu, Yi and Chen, Yaran and Zhao, Dongbin and Li, Dong},
  year = {2021},
  month = jan,
  journal = {Neurocomputing},
  volume = {421},
  pages = {140--150},
  issn = {0925-2312}
}

@inproceedings{luber2010PeopleTracking,
  title = {People Tracking with Human Motion Predictions from Social Forces},
  booktitle = {Proceedings of the 2010 {{IEEE International Conference}} on {{Robotics}} and {{Automation}} ({{ICRA}})},
  author = {Luber, Matthias and Stork, Johannes A. and Tipaldi, Gian Diego and Arras, Kai O.},
  year = {2010},
  pages = {464--469},
  issn = {1050-4729},
  doi = {10.1109/ROBOT.2010.5509779}
}

@article{mac2017HierarchicalGlobal,
  title = {A Hierarchical Global Path Planning Approach for Mobile Robots Based on Multi-Objective Particle Swarm Optimization},
  author = {Mac, Thi Thoa and Copot, Cosmin and Tran, Duc Trung and Keyser, Robin De},
  year = {2017},
  journal = {Applied Soft Computing},
  volume = {59},
  pages = {68--76},
  issn = {1568-4946}
}

@article{madras1988PivotAlgorithm,
  title = {The Pivot Algorithm: {{A}} Highly Efficient {{Monte Carlo}} Method for the Self-Avoiding Walk},
  shorttitle = {The Pivot Algorithm},
  author = {Madras, Neal and Sokal, Alan D.},
  year = {1988},
  month = jan,
  journal = {Journal of Statistical Physics},
  volume = {50},
  number = {1},
  pages = {109--186},
  issn = {1572-9613},
  doi = {10.1007/BF01022990}
}

@inproceedings{mangalam2021GoalsWaypoints,
  title = {From {{Goals}}, Waypoints \& Paths to Long Term Human Trajectory Forecasting},
  booktitle = {Proceedings of the 2021 {{IEEE}}/{{CVF International Conference}} on {{Computer Vision}} ({{ICCV}})},
  author = {Mangalam, Karttikeya and An, Yang and Girase, Harshayu and Malik, Jitendra},
  year = {2021},
  pages = {15213--15222},
  doi = {10.1109/ICCV48922.2021.01495},
  isbn = {978-1-66542-812-5}
}

@article{mirowski2016Learningarxiv,
  title = {Learning to Navigate in Complex Environments},
  author = {Mirowski, Piotr and Pascanu, Razvan and Viola, Fabio and Soyer, Hubert and Ballard, Andrew J and Banino, Andrea and Denil, Misha and Goroshin, Ross and Sifre, Laurent and Kavukcuoglu, Koray and Kumaran, Dharshan and Hadsell, Raia},
  year = {2016},
  journal = {arXiv preprint arXiv:1611.03673},
  eprint = {1611.03673},
  eprinttype = {arxiv},
  archiveprefix = {arXiv}
}

@inproceedings{mirowski2017LearningNavigate,
  title = {Learning to Navigate in Complex Environments},
  booktitle = {Proceedings of the 2017 {{International Conference}} on {{Learning Representations}} ({{ICLR}})},
  author = {Mirowski, Piotr and Pascanu, Razvan and Viola, Fabio and Soyer, Hubert and Ballard, Andy and Banino, Andrea and Denil, Misha and Goroshin, Ross and Sifre, Laurent and Kavukcuoglu, Koray and Kumaran, Dharshan and Hadsell, Raia},
  year = {2017}
}

@article{mnih2015HumanlevelControl,
  title = {Human-Level Control through Deep Reinforcement Learning},
  author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and et al.},
  year = {2015},
  month = feb,
  journal = {Nature},
  volume = {518},
  number = {7540},
  pages = {529--533},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/nature14236},
  copyright = {2015 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.}
}

@article{mnih2015HumanlevelControl1,
  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 Petersen, Stig and Beattie, Charles and Sadik, Amir and Antonoglou, Ioannis and King, Helen and Kumaran, Dharshan and Wierstra, Daan and Legg, Shane and Hassabis, Demis},
  year = {2015},
  month = feb,
  journal = {Nature},
  volume = {518},
  number = {7540},
  pages = {529--533},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/nature14236},
  copyright = {2015 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.}
}

@article{mur2015ORBSLAM,
  title = {{{ORB-SLAM}}: {{A}} Versatile and Accurate Monocular {{SLAM}} System},
  author = {{Mur-Artal}, Ra{\'u}l and Montiel, J. M. M. and Tard{\'o}s, Juan D.},
  year = {2015},
  month = oct,
  journal = {IEEE Transactions on Robotics},
  volume = {31},
  number = {5},
  pages = {1147--1163},
  doi = {10.1109/TRO.2015.2463671}
}

@inproceedings{nachum2018DataEfficientHierarchical,
  title = {Data-Efficient Hierarchical Reinforcement Learning},
  booktitle = {Proceedings of the 2018 {{Neural Information Processing Systems}} ({{NeurIPS}})},
  author = {Nachum, Ofir and Gu, Shixiang and Lee, Honglak and Levine, Sergey},
  year = {2018},
  pages = {3307--3317}
}

@article{narvekar2022CurriculumLearning,
  title = {Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey},
  shorttitle = {Curriculum Learning for Reinforcement Learning Domains},
  author = {Narvekar, Sanmit and Peng, Bei and Leonetti, Matteo and Sinapov, Jivko and Taylor, Matthew E. and Stone, Peter},
  year = {2022},
  month = jun,
  journal = {The Journal of Machine Learning Research},
  volume = {21},
  number = {1},
  pages = {181:7382--181:7431},
  issn = {1532-4435}
}

@inproceedings{pellegrini2009YouLl,
  title = {You'll Never Walk Alone: {{Modeling}} Social Behavior for Multi-Target Tracking},
  shorttitle = {You'll Never Walk Alone},
  booktitle = {Proceedings of the 2009 {{IEEE International Conference}} on {{Computer Vision}} ({{ICCV}})},
  author = {Pellegrini, S. and Ess, A. and Schindler, K. and {van Gool}, L.},
  year = {2009},
  pages = {261--268},
  issn = {2380-7504},
  doi = {10.1109/ICCV.2009.5459260}
}

@article{rajeswaran2017LearningComplex,
  title = {Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations},
  author = {Rajeswaran, Aravind and Kumar, Vikash and Gupta, Abhishek and Vezzani, Giulia and Schulman, John and Todorov, Emanuel and Levine, Sergey},
  year = {2017},
  journal = {arXiv preprint arXiv:1709.10087},
  eprint = {1709.10087},
  eprinttype = {arxiv},
  doi = {10.48550/arXiv.1709.10087},
  archiveprefix = {arXiv}
}

@article{rao2021VisualNavigation,
  title = {Visual Navigation with Multiple Goals Based on Deep Reinforcement Learning},
  author = {Rao, Zhenhuan and Wu, Yuechen and Yang, Zifei and Zhang, Wei and Lu, Shijian and Lu, Weizhi and Zha, ZhengJun},
  year = {2021},
  month = dec,
  journal = {IEEE Transactions on Neural Networks and Learning Systems},
  volume = {32},
  number = {12},
  pages = {5445--5455},
  issn = {2162-2388},
  doi = {10.1109/TNNLS.2021.3057424}
}

@inproceedings{robicquet2016LearningSocial,
  title = {Learning Social Etiquette: Human Trajectory Understanding in Crowded Scenes},
  shorttitle = {Learning {{Social Etiquette}}},
  booktitle = {Proceedings of the 2016 {{European}} Conference on Computer Vision ({{ECCV}})},
  author = {Robicquet, Alexandre and Sadeghian, Amir and Alahi, Alexandre and Savarese, Silvio},
  year = {2016},
  pages = {549--565},
  doi = {10.1007/978-3-319-46484-8_33},
  isbn = {978-3-319-46484-8}
}

@inproceedings{rösmann2017OnlineTrajectory,
  title = {Online Trajectory Prediction and Planning for Social Robot Navigation},
  booktitle = {Proceedings of the 2017 {{IEEE International Conference}} on {{Advanced Intelligent Mechatronics}} ({{AIM}})},
  author = {R{\"o}smann, Christoph and Oeljeklaus, Malte and Hoffmann, Frank and Bertram, Torsten},
  year = {2017},
  pages = {1255--1260},
  issn = {2159-6255},
  doi = {10.1109/AIM.2017.8014190}
}

@article{rudenko2020HumanMotion,
  title = {Human Motion Trajectory Prediction: {{A}} Survey},
  shorttitle = {Human {{Motion Trajectory Prediction}}},
  author = {Rudenko, Andrey and Palmieri, Luigi and Herman, Michael and Kitani, Kris M. and Gavrila, Dariu M. and Arras, Kai O.},
  year = {2020},
  month = jul,
  journal = {The International Journal of Robotics Research},
  volume = {39},
  number = {8},
  eprint = {1905.06113},
  eprinttype = {arxiv},
  primaryclass = {cs},
  pages = {895--935},
  issn = {0278-3649, 1741-3176},
  doi = {10.1177/0278364920917446},
  archiveprefix = {arXiv}
}

@article{schulman2017ProximalPolicy,
  title = {Proximal Policy Optimization Algorithms},
  author = {Schulman, John and Wolski, Filip and Dhariwal, Prafulla and Radford, Alec and Klimov, Oleg},
  year = {2017},
  journal = {arXiv preprint arXiv:1707.06347},
  eprint = {1707.06347},
  eprinttype = {arxiv},
  archiveprefix = {arXiv}
}

@inproceedings{shen2011AutonomousMultifloor,
  title = {Autonomous Multi-Floor Indoor Navigation with a Computationally Constrained {{MAV}}},
  booktitle = {Proceedings of the 2011 {{IEEE International Conference}} on {{Robotics}} and {{Automation}} ({{ICRA}})},
  author = {Shen, Shaojie and Michael, Nathan and Kumar, Vijay},
  year = {2011},
  pages = {20--25},
  issn = {1050-4729},
  doi = {10.1109/ICRA.2011.5980357}
}

@article{silver2016MasteringGame,
  title = {Mastering the Game of {{Go}} with Deep Neural Networks and Tree Search},
  author = {Silver, David and Huang, Aja and Maddison, Chris J. and et al.},
  year = {2016},
  month = jan,
  journal = {Nature},
  volume = {529},
  number = {7587},
  pages = {484--489},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/nature16961},
  copyright = {2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.}
}

@article{silver2016MasteringGame1,
  title = {Mastering the Game of {{Go}} with Deep Neural Networks and Tree Search},
  author = {Silver, David and Huang, Aja and Maddison, Chris J. and Guez, Arthur and Sifre, Laurent and {van den Driessche}, George and Schrittwieser, Julian and Antonoglou, Ioannis and Panneershelvam, Veda and Lanctot, Marc and Dieleman, Sander and Grewe, Dominik and Nham, John and Kalchbrenner, Nal and Sutskever, Ilya and Lillicrap, Timothy and Leach, Madeleine and Kavukcuoglu, Koray and Graepel, Thore and Hassabis, Demis},
  year = {2016},
  month = jan,
  journal = {Nature},
  volume = {529},
  number = {7587},
  pages = {484--489},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/nature16961},
  copyright = {2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.}
}

@article{silver2017MasteringGame,
  title = {Mastering the Game of {{Go}} without Human Knowledge},
  author = {Silver, David and Schrittwieser, Julian and Simonyan, Karen and et al.},
  year = {2017},
  month = oct,
  journal = {Nature},
  volume = {550},
  number = {7676},
  pages = {354--359},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/nature24270},
  copyright = {2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.}
}

@article{silver2017MasteringGame1,
  title = {Mastering the Game of {{Go}} without Human Knowledge},
  author = {Silver, David and Schrittwieser, Julian and Simonyan, Karen and Antonoglou, Ioannis and Huang, Aja and Guez, Arthur and Hubert, Thomas and Baker, Lucas and Lai, Matthew and Bolton, Adrian and Chen, Yutian and Lillicrap, Timothy and Hui, Fan and Sifre, Laurent and {van den Driessche}, George and Graepel, Thore and Hassabis, Demis},
  year = {2017},
  month = oct,
  journal = {Nature},
  volume = {550},
  number = {7676},
  pages = {354--359},
  publisher = {{Nature Publishing Group}},
  issn = {1476-4687},
  doi = {10.1038/nature24270},
  copyright = {2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.}
}

@article{sutton1999MDPsSemiMDPs,
  title = {Between {{MDPs}} and Semi-{{MDPs}}: {{A}} Framework for Temporal Abstraction in Reinforcement Learning},
  shorttitle = {Between {{MDPs}} and Semi-{{MDPs}}},
  author = {Sutton, Richard S. and Precup, Doina and Singh, Satinder},
  year = {1999},
  month = aug,
  journal = {Artificial Intelligence},
  volume = {112},
  number = {1-2},
  pages = {181--211},
  issn = {00043702}
}

@inproceedings{tai2017VirtualtorealDeep,
  title = {Virtual-to-Real Deep Reinforcement Learning: {{Continuous}} Control of Mobile Robots for Mapless Navigation},
  shorttitle = {Virtual-to-Real Deep Reinforcement Learning},
  booktitle = {Proceedings of the 2017 {{IEEE}}/{{RSJ International Conference}} on {{Intelligent Robots}} and {{Systems}} ({{IROS}})},
  author = {Tai, Lei and Paolo, Giuseppe and Liu, Ming},
  year = {2017},
  pages = {31--36},
  doi = {10.1109/IROS.2017.8202134},
  isbn = {978-1-5386-2682-5}
}

@inproceedings{tang2020ReinforcementCoLearning,
  title = {Reinforcement Co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware},
  booktitle = {Proceedings of the 2020 {{IEEE}}/{{RSJ International Conference}} on {{Intelligent Robots}} and {{Systems}} ({{IROS}})},
  author = {Tang, Guangzhi and Kumar, Neelesh and Michmizos, Konstantinos P.},
  year = {2020},
  pages = {6090--6097},
  doi = {10.1109/IROS45743.2020.9340948},
  isbn = {978-1-72816-212-6}
}

@inproceedings{vasquez2016NovelPlanningbased,
  title = {Novel Planning-Based Algorithms for Human Motion Prediction},
  booktitle = {Proceedings of the 2016 {{IEEE International Conference}} on {{Robotics}} and {{Automation}} ({{ICRA}})},
  author = {Vasquez, Dizan},
  year = {2016},
  pages = {3317--3322},
  doi = {10.1109/ICRA.2016.7487505}
}

@inproceedings{vezhnevets2017FeUdalNetworks,
  title = {{{FeUdal}} Networks for Hierarchical Reinforcement Learning},
  booktitle = {Proceedings of the 2017 {{International Conference}} on {{Machine Learning}} ({{ICML}})},
  author = {Vezhnevets, Alexander Sasha and Osindero, Simon and Schaul, Tom and Heess, Nicolas and Jaderberg, Max and Silver, David and Kavukcuoglu, Koray},
  year = {2017},
  pages = {3540--3549},
  issn = {2640-3498}
}

@article{vinyals2019GrandmasterLevel,
  title = {Grandmaster Level in {{StarCraft II}} Using Multi-Agent Reinforcement Learning},
  author = {Vinyals, Oriol and Babuschkin, Igor and Czarnecki, Wojciech M. and et al.},
  year = {2019},
  month = nov,
  journal = {Nature},
  volume = {575},
  number = {7782},
  pages = {350--354},
  issn = {1476-4687},
  doi = {10.1038/s41586-019-1724-z}
}

@article{vinyals2019GrandmasterLevel1,
  title = {Grandmaster Level in {{StarCraft II}} Using Multi-Agent Reinforcement Learning},
  author = {Vinyals, Oriol and Babuschkin, Igor and Czarnecki, Wojciech M. and Mathieu, Micha{\"e}l and Dudzik, Andrew and Chung, Junyoung and Choi, David H. and Powell, Richard and Ewalds, Timo and Georgiev, Petko and Oh, Junhyuk and Horgan, Dan and Kroiss, Manuel and Danihelka, Ivo and Huang, Aja and Sifre, Laurent and Cai, Trevor and Agapiou, John P. and Jaderberg, Max and Vezhnevets, Alexander S. and Leblond, R{\'e}mi and Pohlen, Tobias and Dalibard, Valentin and Budden, David and Sulsky, Yury and Molloy, James and Paine, Tom L. and Gulcehre, Caglar and Wang, Ziyu and Pfaff, Tobias and Wu, Yuhuai and Ring, Roman and Yogatama, Dani and W{\"u}nsch, Dario and McKinney, Katrina and Smith, Oliver and Schaul, Tom and Lillicrap, Timothy and Kavukcuoglu, Koray and Hassabis, Demis and Apps, Chris and Silver, David},
  year = {2019},
  month = nov,
  journal = {Nature},
  volume = {575},
  number = {7782},
  pages = {350--354},
  issn = {1476-4687},
  doi = {10.1038/s41586-019-1724-z}
}

@inproceedings{warren1993FastPath,
  title = {Fast Path Planning Using Modified {{A}}* Method},
  booktitle = {Proceedings of  the 1993 {{IEEE International Conference}} on {{Robotics}} and {{Automation}} ({{ICRA}})},
  author = {Warren, C.W.},
  year = {1993},
  pages = {662-667 vol.2},
  doi = {10.1109/ROBOT.1993.291883}
}

@article{wu2018BuildingGeneralizable,
  title = {Building Generalizable Agents with a Realistic and Rich 3d Environment},
  author = {Wu, Yi and Wu, Yuxin and Gkioxari, Georgia and Tian, Yuandong},
  year = {2018},
  journal = {arXiv preprint arXiv:1801.02209},
  eprint = {1801.02209},
  eprinttype = {arxiv},
  archiveprefix = {arXiv}
}

@article{zeng2020VisualNavigation,
  title = {Visual Navigation with Asynchronous Proximal Policy Optimization in Artificial Agents},
  author = {Zeng, Fanyu and Wang, Chen},
  year = {2020},
  month = oct,
  journal = {Journal of Robotics},
  volume = {2020},
  pages = {8702962},
  publisher = {{Hindawi}},
  issn = {1687-9600},
  doi = {10.1155/2020/8702962}
}

@article{zhou2011BezierCurve,
  title = {B\'ezier Curve Based Smooth Path Planning for Mobile Robot},
  author = {Zhou, F. and Song, Baoye and Tian, Guohui},
  year = {2011},
  month = dec,
  journal = {Journal of Information and Computational Science},
  volume = {8},
  number = {12},
  pages = {2441--2450}
}

@inproceedings{zhu2017TargetdrivenVisual,
  title = {Target-Driven Visual Navigation in Indoor Scenes Using Deep Reinforcement Learning},
  booktitle = {Proceedings of the 2017 {{IEEE International Conference}} on {{Robotics}} and {{Automation}} ({{ICRA}})},
  author = {Zhu, Yuke and Mottaghi, Roozbeh and Kolve, Eric and Lim, Joseph J. and Gupta, Abhinav and {Fei-Fei}, Li and Farhadi, Ali},
  year = {2017},
  pages = {3357--3364},
  doi = {10.1109/ICRA.2017.7989381},
  isbn = {978-1-5090-4633-1}
}

@article{manela2022CurriculumLearning,
  title = {Curriculum Learning with {{Hindsight Experience Replay}} for Sequential Object Manipulation Tasks},
  author = {Manela, B. and Biess, A.},
  year = {2022},
  journal = {Neural Networks},
  volume = {145},
  pages = {260--270},
  issn = {0893-6080}
}
