
@article{TD3,
	author       = {Scott Fujimoto and
	Herke van Hoof and
	David Meger},
	title        = {Addressing Function Approximation Error in Actor-Critic Methods},
	journal      = {CoRR},
	volume       = {abs/1802.09477},
	year         = {2018},
	url          = {http://arxiv.org/abs/1802.09477},
	eprinttype    = {arXiv},
	eprint       = {1802.09477},
	timestamp    = {Sat, 28 Sep 2019 00:58:01 +0200},
	biburl       = {https://dblp.org/rec/journals/corr/abs-1802-09477.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
};

@InProceedings{pmlr-v32-silver14,
  title = 	 {Deterministic Policy Gradient Algorithms},
  author = 	 {Silver, David and Lever, Guy and Heess, Nicolas and Degris, Thomas and Wierstra, Daan and Riedmiller, Martin},
  booktitle = 	 {Proceedings of the 31st International Conference on Machine Learning},
  pages = 	 {387--395},
  year = 	 {2014},
  editor = 	 {Xing, Eric P. and Jebara, Tony},
  volume = 	 {32},
  number =       {1},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Bejing, China},
  month = 	 {22--24 Jun},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v32/silver14.pdf},
  url = 	 {https://proceedings.mlr.press/v32/silver14.html},
  abstract = 	 {In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic policy gradient has a particularly appealing form: it is the expected gradient of the action-value function. This simple form means that the deterministic policy gradient can be estimated much more efficiently than the usual stochastic policy gradient. To ensure adequate exploration, we introduce an off-policy actor-critic algorithm that learns a deterministic target policy from an exploratory behaviour policy. Deterministic policy gradient algorithms outperformed their stochastic counterparts in several benchmark problems, particularly in high-dimensional action spaces.}
};


@article{DBLP:journals/corr/abs-1812-05905,
  author       = {Tuomas Haarnoja and
                  Aurick Zhou and
                  Kristian Hartikainen and
                  George Tucker and
                  Sehoon Ha and
                  Jie Tan and
                  Vikash Kumar and
                  Henry Zhu and
                  Abhishek Gupta and
                  Pieter Abbeel and
                  Sergey Levine},
  title        = {Soft Actor-Critic Algorithms and Applications},
  journal      = {CoRR},
  volume       = {abs/1812.05905},
  year         = {2018},
  url          = {http://arxiv.org/abs/1812.05905},
  eprinttype    = {arXiv},
  eprint       = {1812.05905},
  timestamp    = {Tue, 01 Jan 2019 15:01:25 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1812-05905.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
};


@article{DBLP:journals/corr/SchulmanWDRK17,
  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{EXPERIENCE_REPLAY,
	author = {Lin, Long-Ji},
	title = {Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching},
	year = {1992},
	issue_date = {May 1992},
	publisher = {Kluwer Academic Publishers},
	address = {USA},
	volume = {8},
	number = {3–4},
	issn = {0885-6125},
	url = {https://doi.org/10.1007/BF00992699},
	doi = {10.1007/BF00992699},
	abstract = {To date, reinforcement learning has mostly been studied solving simple learning tasks. Reinforcement learning methods that have been studied so far typically converge slowly. The purpose of this work is thus two-fold: 1) to investigate the utility of reinforcement learning in solving much more complicated learning tasks than previously studied, and 2) to investigate methods that will speed up reinforcement learning.This paper compares eight reinforcement learning frameworks: adaptive heuristic critic (AHC) learning due to Sutton, Q-learning due to Watkins, and three extensions to both basic methods for speeding up learning. The three extensions are experience replay, learning action models for planning, and teaching. The frameworks were investigated using connectionism as an approach to generalization. To evaluate the performance of different frameworks, a dynamic environment was used as a testbed. The environment is moderately complex and nondeterministic. This paper describes these frameworks and algorithms in detail and presents empirical evaluation of the frameworks.},
	journal = {Mach. Learn.},
	month = {may},
	pages = {293–321},
	numpages = {29},
	keywords = {Reinforcement learning, planning, connectionist networks, teaching}
}


@PostedContent{ DQN,
	title={ Playing Atari with Deep Reinforcement Learning },
	authors={ Volodymyr Mnih and Koray Kavukcuoglu and David Silver and Alex Graves and Ioannis Antonoglou and Daan Wierstra and Martin Riedmiller },
	journal={ arXiv: Learning },
	year={ 2013 },
	publisher={ ArXiv },
}


@article{DQN2,
	title={Human-level control through deep reinforcement learning},
	author={Volodymyr Mnih and Koray Kavukcuoglu and David Silver and Andrei A. Rusu and Joel Veness and Marc G. Bellemare and Alex Graves and Martin A. Riedmiller and Andreas Kirkeby Fidjeland and Georg Ostrovski and Stig Petersen and Charlie Beattie and Amir Sadik and Ioannis Antonoglou and Helen King and Dharshan Kumaran and Daan Wierstra and Shane Legg and Demis Hassabis},
	journal={Nature},
	year={2015},
	volume={518},
	pages={529-533},
	url={https://api.semanticscholar.org/CorpusID:205242740}
}

@article{DDQN,
	author       = {Hado van Hasselt and
	Arthur Guez and
	David Silver},
	title        = {Deep Reinforcement Learning with Double Q-learning},
	journal      = {CoRR},
	volume       = {abs/1509.06461},
	year         = {2015},
	url          = {http://arxiv.org/abs/1509.06461},
	eprinttype    = {arXiv},
	eprint       = {1509.06461},
	timestamp    = {Mon, 13 Aug 2018 16:47:32 +0200},
	biburl       = {https://dblp.org/rec/journals/corr/HasseltGS15.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}


@article{DuellingDDQN,
	author       = {Ziyu Wang and
	Nando de Freitas and
	Marc Lanctot},
	title        = {Dueling Network Architectures for Deep Reinforcement Learning},
	journal      = {CoRR},
	volume       = {abs/1511.06581},
	year         = {2015},
	url          = {http://arxiv.org/abs/1511.06581},
	eprinttype    = {arXiv},
	eprint       = {1511.06581},
	timestamp    = {Mon, 13 Aug 2018 16:48:17 +0200},
	biburl       = {https://dblp.org/rec/journals/corr/WangFL15.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{PER,
	author = {Schaul, Tom and Quan, John and Antonoglou, Ioannis and Silver, David},
	year = {2015},
	month = {11},
	pages = {},
	title = {Prioritized Experience Replay}
}

@article{PolicyBased,
	title={Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning},
	author={Ronald J. Williams},
	journal={Machine Learning},
	year={2004},
	volume={8},
	pages={229-256},
	url={https://api.semanticscholar.org/CorpusID:19115634}
}

@inproceedings{DDPG,
	author       = {Timothy P. Lillicrap and
	Jonathan J. Hunt and
	Alexander Pritzel and
	Nicolas Heess and
	Tom Erez and
	Yuval Tassa and
	David Silver and
	Daan Wierstra},
	editor       = {Yoshua Bengio and
	Yann LeCun},
	title        = {Continuous control with deep reinforcement learning},
	booktitle    = {4th International Conference on Learning Representations, {ICLR} 2016,
	San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings},
	year         = {2016},
	url          = {http://arxiv.org/abs/1509.02971},
	timestamp    = {Thu, 25 Jul 2019 14:25:37 +0200},
	biburl       = {https://dblp.org/rec/journals/corr/LillicrapHPHETS15.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{SAC,
	author       = {Tuomas Haarnoja and
	Aurick Zhou and
	Pieter Abbeel and
	Sergey Levine},
	title        = {Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning
	with a Stochastic Actor},
	journal      = {CoRR},
	volume       = {abs/1801.01290},
	year         = {2018},
	url          = {http://arxiv.org/abs/1801.01290},
	eprinttype    = {arXiv},
	eprint       = {1801.01290},
	timestamp    = {Mon, 13 Aug 2018 16:48:10 +0200},
	biburl       = {https://dblp.org/rec/journals/corr/abs-1801-01290.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@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{KF,
	title={Kalman Filter for Robot Vision: A Survey},
	author={Shengyong Chen},
	journal={IEEE Transactions on Industrial Electronics},
	year={2012},
	volume={59},
	pages={4409-4420}
}  


@article{SIMD,
	author={Sergey Gorbunov and Udo Kebschull and Ivan Kisel and Volker Lindenstruth and Walter F. J. M{\"{u}}ller},
	title={Fast SIMDized Kalman filter based track fit},
	journal={Comput. Phys. Commun.},
	volume={178},
	number={5},
	pages={374-383},
	year={2008},
	url= {https://doi.org/10.1016/j.cpc.2007.10.001},
	doi= {10.1016/j.cpc.2007.10.001},
	timestamp= {Sun, 02 Oct 2022 15:32:56 +0200},
	biburl= {https://dblp.org/rec/journals/cphysics/GorbunovKKLM08.bib},
	bibsource= {dblp computer science bibliography, https://dblp.org}
}

@article{gym,
	author       = {Greg Brockman and
	Vicki Cheung and
	Ludwig Pettersson and
	Jonas Schneider and
	John Schulman and
	Jie Tang and
	Wojciech Zaremba},
	title        = {OpenAI Gym},
	journal      = {CoRR},
	volume       = {abs/1606.01540},
	year         = {2016},
	url          = {http://arxiv.org/abs/1606.01540},
	eprinttype    = {arXiv},
	eprint       = {1606.01540},
	timestamp    = {Fri, 08 Nov 2019 12:51:06 +0100},
	biburl       = {https://dblp.org/rec/journals/corr/BrockmanCPSSTZ16.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{airsim,
	author       = {Shital Shah and
	Debadeepta Dey and
	Chris Lovett and
	Ashish Kapoor},
	title        = {AirSim: High-Fidelity Visual and Physical Simulation for Autonomous
	Vehicles},
	journal      = {CoRR},
	volume       = {abs/1705.05065},
	year         = {2017},
	url          = {http://arxiv.org/abs/1705.05065},
	eprinttype    = {arXiv},
	eprint       = {1705.05065},
	timestamp    = {Mon, 13 Aug 2018 16:46:28 +0200},
	biburl       = {https://dblp.org/rec/journals/corr/ShahDLK17.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{potentialfield,
	author = {Koren, Yoram and Borenstein, Johann},
	year = {1991},
	month = {05},
	pages = {1398 - 1404 vol.2},
	title = {Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation},
	volume = {2},
	journal = {Proceedings - IEEE International Conference on Robotics and Automation},
	doi = {10.1109/ROBOT.1991.131810}
}

@article{potentialfield2,
	author = {Tharindu Weerakoon and Kazuo Ishii and Amir Ali Forough Nassiraei},
	doi = {doi:10.1515/jaiscr-2015-0028},
	url = {https://doi.org/10.1515/jaiscr-2015-0028},
	title = {An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock},
	journal = {Journal of Artificial Intelligence and Soft Computing Research},
	number = {3},
	volume = {5},
	year = {2015},
	pages = {189--203}
}

@article{RRT,
	title={Rapidly-exploring random trees : a new tool for path planning},
	author={Steven M. LaValle},
	journal={The annual research report},
	year={1998},
	url={https://api.semanticscholar.org/CorpusID:14744621}
}

@article{RRT2,
	title = {Optimal Path Planning using RRT* based Approaches: A Survey and Future Directions},
	journal = {International Journal of Advanced Computer Science and Applications},
	doi = {10.14569/IJACSA.2016.071114},
	url = {http://dx.doi.org/10.14569/IJACSA.2016.071114},
	year = {2016},
	publisher = {The Science and Information Organization},
	volume = {7},
	number = {11},
	author = {Iram Noreen and Amna Khan and Zulfiqar Habib}
}

@article{Veroni,
	title={Roadmap-Based Path Planning - Using the Voronoi Diagram for a Clearance-Based Shortest Path},
	author={Priyadarshi Bhattacharya and Marina L. Gavrilova},
	journal={IEEE Robotics \& Automation Magazine},
	year={2008},
	volume={15},
	url={https://api.semanticscholar.org/CorpusID:1684741}
}

@Article{Dynamicwild,
	AUTHOR = {Ozaki, Mitsuhiro and Aryal, Jagannath and Fox-Hughes, Paul},
	TITLE = {Dynamic Wildfire Navigation System},
	JOURNAL = {ISPRS International Journal of Geo-Information},
	VOLUME = {8},
	YEAR = {2019},
	NUMBER = {4},
	ARTICLE-NUMBER = {194},
	URL = {https://www.mdpi.com/2220-9964/8/4/194},
	ISSN = {2220-9964},
	DOI = {10.3390/ijgi8040194}
}

@misc{SACNAV,
	title={Deep Reinforcement Learning-based UAV Navigation and Control: A Soft Actor-Critic with Hindsight Experience Replay Approach}, 
	author={Myoung Hoon Lee and Jun Moon},
	year={2021},
	eprint={2106.01016},
	archivePrefix={arXiv},
	primaryClass={eess.SY}
}

@article{decisionmaking,
	author = {Schwarting, Wilko and Alonso-Mora, Javier and Rus, Daniela},
	title = {Planning and Decision-Making for Autonomous Vehicles},
	journal = {Annual Review of Control, Robotics, and Autonomous Systems},
	volume = {1},
	number = {1},
	pages = {187-210},
	year = {2018},
	doi = {10.1146/annurev-control-060117-105157},
	
	URL = { 
	
	https://doi.org/10.1146/annurev-control-060117-105157
	
	
	
	},
	eprint = { 
	
	https://doi.org/10.1146/annurev-control-060117-105157
	
	
	
	}
}


@article{yunlung,
	author       = {Yunlong Song and
	Mats Steinweg and
	Elia Kaufmann and
	Davide Scaramuzza},
	title        = {Autonomous Drone Racing with Deep Reinforcement Learning},
	journal      = {CoRR},
	volume       = {abs/2103.08624},
	year         = {2021},
	url          = {https://arxiv.org/abs/2103.08624},
	eprinttype    = {arXiv},
	eprint       = {2103.08624},
	timestamp    = {Tue, 23 Mar 2021 16:29:47 +0100},
	biburl       = {https://dblp.org/rec/journals/corr/abs-2103-08624.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{kalman,
	author = {Kalman, R. E.},
	title = "{A New Approach to Linear Filtering and Prediction Problems}",
	journal = {Journal of Basic Engineering},
	volume = {82},
	number = {1},
	pages = {35-45},
	year = {1960},
	month = {03},
	issn = {0021-9223},
	doi = {10.1115/1.3662552},
	url = {https://doi.org/10.1115/1.3662552},
	eprint = {https://asmedigitalcollection.asme.org/fluidsengineering/article-pdf/82/1/35/5518977/35\_1.pdf},
}

@article{GDM,
	title = {On the momentum term in gradient descent learning algorithms},
	journal = {Neural Networks},
	volume = {12},
	number = {1},
	pages = {145-151},
	year = {1999},
	issn = {0893-6080},
	doi = {https://doi.org/10.1016/S0893-6080(98)00116-6},
	url = {https://www.sciencedirect.com/science/article/pii/S0893608098001166},
	author = {Ning Qian},
	keywords = {Momentum, Gradient descent learning algorithm, Damped harmonic oscillator, Critical damping, Learning rate, Speed of convergence}
}

@inproceedings{SGD,
	title={Stochastic Gradient Learning in Neural Networks},
	author={L{\'e}on Bottou},
	year={1991},
	url={https://api.semanticscholar.org/CorpusID:12410481}
}

@inproceedings{SDG2,
	title={Online Learning and Stochastic Approximations},
	author={L Eon Bottou},
	year={1998},
	url={https://api.semanticscholar.org/CorpusID:2101184}
}

@inproceedings{Adam,
	author       = {Diederik P. Kingma and
	Jimmy Ba},
	editor       = {Yoshua Bengio and
	Yann LeCun},
	title        = {Adam: {A} Method for Stochastic Optimization},
	booktitle    = {3rd International Conference on Learning Representations, {ICLR} 2015,
	San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings},
	year         = {2015},
	url          = {http://arxiv.org/abs/1412.6980},
	timestamp    = {Thu, 25 Jul 2019 14:25:37 +0200},
	biburl       = {https://dblp.org/rec/journals/corr/KingmaB14.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{MinimumSnapTraj,
	title={Minimum snap trajectory generation and control for quadrotors},
	author={Daniel Mellinger and Vijay R. Kumar},
	journal={2011 IEEE International Conference on Robotics and Automation},
	year={2011},
	pages={2520-2525},
	url={https://api.semanticscholar.org/CorpusID:18169351}
}

@Article{MinimumSnapTraj2,
	AUTHOR = {Arshad, Muhammad Awais and Ahmed, Jamal and Bang, Hyochoong},
	TITLE = {Quadrotor Path Planning and Polynomial Trajectory Generation Using Quadratic Programming for Indoor Environments},
	JOURNAL = {Drones},
	VOLUME = {7},
	YEAR = {2023},
	NUMBER = {2},
	ARTICLE-NUMBER = {122},
	URL = {https://www.mdpi.com/2504-446X/7/2/122},
	ISSN = {2504-446X},
	DOI = {10.3390/drones7020122}
}

@ARTICLE{MinimumTimeTraj,
	author={Penicka, Robert and Scaramuzza, Davide},
	journal={IEEE Robotics and Automation Letters}, 
	title={Minimum-Time Quadrotor Waypoint Flight in Cluttered Environments}, 
	year={2022},
	volume={7},
	number={2},
	pages={5719-5726},
	doi={10.1109/LRA.2022.3154013}}
	
@ARTICLE{MinimumTimeTraj2,
	author={Penicka, Robert and Song, Yunlong and Kaufmann, Elia and Scaramuzza, Davide},
	journal={IEEE Robotics and Automation Letters}, 
	title={Learning Minimum-Time Flight in Cluttered Environments}, 
	year={2022},
	volume={7},
	number={3},
	pages={7209-7216},
	doi={10.1109/LRA.2022.3181755}}


@article{MPCC,
	author       = {Angel Romero and
	Sihao Sun and
	Philipp Foehn and
	Davide Scaramuzza},
	title        = {Model Predictive Contouring Control for Near-Time-Optimal Quadrotor
	Flight},
	journal      = {CoRR},
	volume       = {abs/2108.13205},
	year         = {2021},
	url          = {https://arxiv.org/abs/2108.13205},
	eprinttype    = {arXiv},
	eprint       = {2108.13205},
	timestamp    = {Thu, 02 Sep 2021 14:42:29 +0200},
	biburl       = {https://dblp.org/rec/journals/corr/abs-2108-13205.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{TOP,
	author       = {Philipp Foehn and
	Angel Romero and
	Davide Scaramuzza},
	title        = {Time-Optimal Planning for Quadrotor Waypoint Flight},
	journal      = {CoRR},
	volume       = {abs/2108.04537},
	year         = {2021},
	url          = {https://arxiv.org/abs/2108.04537},
	eprinttype    = {arXiv},
	eprint       = {2108.04537},
	timestamp    = {Wed, 11 Aug 2021 15:24:08 +0200},
	biburl       = {https://dblp.org/rec/journals/corr/abs-2108-04537.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DDR,
	author       = {Antonio Loquercio and
	Elia Kaufmann and
	Ren{\'{e}} Ranftl and
	Alexey Dosovitskiy and
	Vladlen Koltun and
	Davide Scaramuzza},
	title        = {Deep Drone Racing: From Simulation to Reality with Domain Randomization},
	journal      = {CoRR},
	volume       = {abs/1905.09727},
	year         = {2019},
	url          = {http://arxiv.org/abs/1905.09727},
	eprinttype    = {arXiv},
	eprint       = {1905.09727},
	timestamp    = {Mon, 09 Nov 2020 15:07:11 +0100},
	biburl       = {https://dblp.org/rec/journals/corr/abs-1905-09727.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{RTNMPC,
	author={Salzmann, Tim and Kaufmann, Elia and Arrizabalaga, Jon and Pavone, Marco and Scaramuzza, Davide and Ryll, Markus},
	journal={IEEE Robotics and Automation Letters}, 
	title={Real-Time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms}, 
	year={2023},
	volume={8},
	number={4},
	pages={2397-2404},
	doi={10.1109/LRA.2023.3246839}}


@ARTICLE{NMPC,
	author={Sun, Sihao and Romero, Angel and Foehn, Philipp and Kaufmann, Elia and Scaramuzza, Davide},
	journal={IEEE Transactions on Robotics}, 
	title={A Comparative Study of Nonlinear MPC and Differential-Flatness-Based Control for Quadrotor Agile Flight}, 
	year={2022},
	volume={38},
	number={6},
	pages={3357-3373},
	doi={10.1109/TRO.2022.3177279}}

@article{PSMPC,
	author       = {Yunlong Song and
	Davide Scaramuzza},
	title        = {Policy Search for Model Predictive Control with Application to Agile
	Drone Flight},
	journal      = {CoRR},
	volume       = {abs/2112.03850},
	year         = {2021},
	url          = {https://arxiv.org/abs/2112.03850},
	eprinttype    = {arXiv},
	eprint       = {2112.03850},
	timestamp    = {Mon, 13 Dec 2021 17:51:48 +0100},
	biburl       = {https://dblp.org/rec/journals/corr/abs-2112-03850.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}