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Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their potential for many real-world tasks.
Consideration of risk in reinforcement learning
Matthias Heger · 1994
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Constrained Markov decision processes with total cost criteria: Lagrangian approach and dual linear program
Eitan Altman · 1998
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Complexity Theory: Quadratic Programming
Stephen A. Vavasis · 2001
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Constructive safety using control barrier functions
Peter Wieland and Frank Allgöwer · 2007
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Principles of Model Checking
Christel Baier and Joost-Pieter Katoen · 2008
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Differential dynamic logic for hybrid systems
André Platzer · 2008
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Provably safe navigation for mobile robots with limited field-of-views in unknown dynamic environments
Sara Bouraine, Thierry Fraichard, and Hassen Salhi · 2012
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Reducing conservativeness in safety guarantees by learning disturbances online: Iterated guaranteed safe online learning
Jeremy H. Gillula and Claire J. Tomlin · 2013
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Playing Atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Reachability-based safe learning with Gaussian processes
Anayo K. Akametalu, Shahab Kaynama, Jaime F. Fisac, Melanie N. Zeilinger, Jeremy H. Gillula, and Claire J. Tomlin · 2014
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On real-time robust model predictive control
Melanie N. Zeilinger, Davide M. Raimondo, Alexander Domahidi, Manfred Morari, and Colin N. Jones · 2014
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An introduction to CORA 2015
Matthias Althoff · 2015
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A comprehensive survey on safe reinforcement learning
Javier García and Fernando Fernández · 2015
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Q-learning for robust satisfaction of signal temporal logic specifications
Derya Aksaray, Austin Jones, Zhaodan Kong, Mac Schwager, and Calin Belta · 2016
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Safe exploration in finite Markov decision processes with Gaussian processes
Matteo Turchetta, Felix Berkenkamp, and Andreas Krause · 2016
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Safe model-based reinforcement learning with stability guarantees
Felix Berkenkamp, Angela P. Schoellig, Matteo Turchetta, and Andreas Krause · 2017
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Deep reinforcement learning from human preferences
Paul F. Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Reinforcement learning with temporal logic rewards
Xiao Li, Cristian-Ioan Vasile, and Calin Belta · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Reinforcement learning based algorithm with safety handling and risk perception
Suhas Shyamsundar, Tommaso Mannucci, and Erik-Jan Van Kampen · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George Van Den Driessche, Thore Graepel, and Demis Hassabis · 2017
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Safe reinforcement learning via shielding
Mohammed Alshiekh, Roderick Bloem, Rüdiger Ehlers, Bettina Könighofer, Scott Niekum, and Ufuk Topcu · 2018
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Verifiable reinforcement learning via policy extraction
Osbert Bastani, Yewen Pu, and Armando Solar-Lezama · 2018
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Hamilton-Jacobi reachability: Some recent theoretical advances and applications in unmanned airspace management
Mo Chen and Claire J. Tomlin · 2018
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A Lyapunov-based approach to safe reinforcement learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, and Mohammad Ghavamzadeh · 2018
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Safe exploration in continuous action spaces
Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik, Todd Hester, Cosmin Paduraru, and Yuval Tassa · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Van Hoof, and David Meger · 2018
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Safe reinforcement learning via formal methods: Toward safe control through proof and learning
Nathan Fulton and André Platzer · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Safe exploration algorithms for reinforcement learning controllers
Tommaso Mannucci, Erik-Jan van Kampen, Cornelis de Visser, and Qiping Chu · 2018
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High-level decision making for safe and reasonable autonomous lane changing using reinforcement learning
Branka Mirchevska, Christian Pek, Moritz Werling, Matthias Althoff, and Joschka Boedecker · 2018
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Assessing generalization in deep reinforcement learning, 2018
Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun, and Dawn Song · 2018
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OptLayer - practical constrained optimization for deep reinforcement learning in the real world
Tu-Hoa Pham, Giovanni De Magistris, and Ryuki Tachibana · 2018
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Reachset model predictive control for disturbed nonlinear systems
Bastian Schürmann, Niklas Kochdumper, and Matthias Althoff · 2018
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Online algorithms for POMDPs with continuous state, action, and observation spaces
Zachary Sunberg and Mykel Kochenderfer · 2018
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Reinforcement Leaning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
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Control barrier functions: Theory and applications
Aaron D. Ames, Samuel Coogan, Magnus Egerstedt, Gennaro Notomista, Koushil Sreenath, and Paulo Tabuada · 2019
Cited alongside, same era.
LTL and beyond: Formal languages for reward function specification in reinforcement learning
Alberto Camacho, Rodrigo Toro Icarte, Toryn Q Klassen, Richard Valenzano, and Sheila A McIlraith · 2019
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End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks
Richard Cheng, Gábor Orosz, Richard M Murray, and Joel W Burdick · 2019
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A general safety framework for learning-based control in uncertain robotic systems
Jaime F. Fisac, Anayo K. Akametalu, Melanie N. Zeilinger, Shahab Kaynama, Jeremy Gillula, and Claire J. Tomlin · 2019
Cited alongside, same era.
Verifiably safe off-model reinforcement learning
Nathan Fulton and André Platzer · 2019
Cited alongside, same era.
Can you trust your autonomous car? interpretable and verifiably safe reinforcement learning
Lukas M. Schmidt, Georgios D. Kontes, Axel Plinge, and Christopher Mutschler · 2021
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Reachability-based trajectory safeguard (RTS): A safe and fast reinforcement learning safety layer for continuous control
Yifei Simon Shao, Chao Chen, Shreyas Kousik, and Ram Vasudevan · 2021
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A control barrier perspective on episodic learning via projection-to-state safety
Andrew J. Taylor, Andrew Singletary, Yisong Yue, and Aaron D. Ames · 2021
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Recovery RL: Safe reinforcement learning with learned recovery zones
Brijen Thananjeyan, Ashwin Balakrishna, Suraj Nair, Michael Luo, Krishnan Srinivasan, Minho Hwang, Joseph E. Gonzalez, Julian Ibarz, Chelsea Finn, and Ken Goldberg · 2021
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A predictive safety filter for learning-based control of constrained nonlinear dynamical systems
Kim P. Wabersich and Melanie N. Zeilinger · 2021
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Omega-regular objectives in model-free reinforcement learning
Ernst Moritz Hahn, Mateo Perez, Sven Schewe, Fabio Somenzi, Ashutosh Trivedi, and Dominik Wojtczak · 2019
Cited alongside, same era.
Invariant, viability and discriminating kernel under-approximation via zonotope scaling
Ian M. Mitchell, Jacob Budzis, and Andriy Bolyachevets · 2019
Cited alongside, same era.
Model conformance for cyber-physical systems: A survey
Hendrik Roehm, Jens Oehlerking, Matthias Woehrle, and Matthias Althoff · 2019
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Neurosymbolic reinforcement learning with formally verified exploration
Greg Anderson, Abhinav Verma, Isil Dillig, and Swarat Chaudhuri · 2020
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Autonomous driving using safe reinforcement learning by incorporating a regret-based human lane-changing decision model
Dong Chen, Longsheng Jiang, Yue Wang, and Zhaojian Li · 2020
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Towards safe human-robot collaboration using deep reinforcement learning
Mohamed El-Shamouty, Xinyang Wu, Shanqi Yang, Marcel Albus, and Marco F. Huber · 2020
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Safe reinforcement learning via projection on a safe set: How to achieve optimality?
Sebastien Gros, Mario Zanon, and Alberto Bemporad · 2020
Cited alongside, same era.
Safe reinforcement learning for CPSs via formal modeling and verification
Chenchen Yang, Jing Liu, Haiying Sun, Junfeng Sun, Xiang Chen, and Lipeng Zhang · 2021
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A survey of deep reinforcement learning algorithms for motion planning and control of autonomous vehicles
Fei Ye, Shen Zhang, Pin Wang, and Ching Yao Chan · 2021
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Safe reinforcement learning using robust MPC
Mario Zanon and Sebastien Gros · 2021
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A Framework for Transforming Specifications in Reinforcement Learning , pp. 604–624
Rajeev Alur, Suguman Bansal, Osbert Bastani, and Kishor Jothimurugan · 2022
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Safe learning in robotics: From learning-based control to safe reinforcement learning
Lukas Brunke, Melissa Greeff, Adam W. Hall, Zhaocong Yuan, Siqi Zhou, Jacopo Panerati, and Angela P. Schoellig · 2022
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Contingency-constrained economic dispatch with safe reinforcement learning
Michael Eichelbeck, Hannah Markgraf, and Matthias Althoff · 2022
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LCRL: Certified policy synthesis via logically-constrained reinforcement learning
Mohammadhosein Hasanbeig, Daniel Kroening, and Alessandro Abate · 2022
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A closer look at invalid action masking in policy gradient algorithms
Shengyi Huang and Santiago Ontañón · 2022
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Deep reinforcement learning for autonomous driving: A survey
B. Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A.Al Sallab, Senthil Yogamani, and Patrick Perez · 2022
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Safe reinforcement learning for urban driving using invariably safe braking sets
Hanna Krasowski, Yinqiang Zhang, and Matthias Althoff · 2022
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Goal misgeneralization in deep reinforcement learning
Lauro Langosco Di Langosco, Jack Koch, Lee D. Sharkey, Jacob Pfau, and David Krueger · 2022
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ADAS-RL: Safety learning approach for stable autonomous driving
Dongsu Lee and Minhae Kwon · 2022
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Reinforcement learning with safety and stability guarantees during exploration for linear systems
Zahra Marvi and Bahare Kiumarsi · 2022
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Safe and psychologically pleasant traffic signal control with reinforcement learning using action masking
Arthur Müller and Matthia Sabatelli · 2022
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Shielded deep reinforcement learning for multi-sensor spacecraft imaging
Islam Nazmy, Andrew Harris, Morteza Lahijanian, and Hanspeter Schaub · 2022
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Defining and characterizing reward hacking
Joar Max Viktor Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, and David Krueger · 2022
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Computationally efficient safe reinforcement learning for power systems
Daniel Tabas and Baosen Zhang · 2022
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Provably safe deep reinforcement learning for robotic manipulation in human environments
Jakob Thumm and Matthias Althoff · 2022
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A safe training approach for deep reinforcement learning-based traffic engineering
Linghao Wang, Miao Wang, and Yujun Zhang · 2022
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Ensuring safety of learning-based motion planners using control barrier functions
Xiao Wang · 2022
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On the (in)tractability of reinforcement learning for LTL objectives
Cambridge Yang, Michael L. Littman, and Michael Carbin · 2022
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Policy synthesis and reinforcement learning for discounted LTL
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Safe reinforcement learning for multi-energy management systems with known constraint functions
Glenn Ceusters, Luis Ramirez Camargo, Rüdiger Franke, Ann Nowé, and Maarten Messagie · 2023
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Safe reinforcement learning for high-speed autonomous racing
Benjamin D. Evans, Hendrik W. Jordaan, and Herman A. Engelbrecht · 2023
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Provably safe reinforcement learning via action projection using reachability analysis and polynomial zonotopes
Niklas Kochdumper, Hanna Krasowski, Xiao Wang, Stanley Bak, and Matthias Althoff · 2023
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Safe deep reinforcement learning-based constrained optimal control scheme for HEV energy management
Zemin Eitan Liu, Quan Zhou, Yanfei Li, Shijin Shuai, and Hongming Xu · 2023
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Safe deep reinforcement learning in diesel engine emission control
Armin Norouzi, Saeid Shahpouri, David Gordon, Mahdi Shahbakhti, and Charles Robert Koch · 2023
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Scalable computation of robust control invariant sets of nonlinear systems
Lukas Schäfer, Felix Gruber, and Matthias Althoff · 2023
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Data-driven safety filters: Hamilton-Jacobi reachability, control barrier functions, and predictive methods for uncertain systems
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SRL-TR2: A safe reinforcement learning based trajectory tracker framework
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District cooling system control for providing operating reserve based on safe deep reinforcement learning
Peipei Yu, Hongcai Zhang, Yonghua Song, Hongxun Hui, and Ge Chen · 2023
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Ev charging command fast allocation approach based on deep reinforcement learning with safety modules
Jin Zhang, Yuxiang Guan, Liang Che, and Mohammad Shahidehpour · 2023
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