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Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment.
Game theory
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Behavioral cloning of student pilots with modular neural networks
Charles W Anderson, Bruce A Draper, and David A Peterson · 2000
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Reinforcement learning with human teachers: evidence of feedback and guidance with implications for learning performance
Andrea L Thomaz and Cynthia Breazeal · 2006
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Interactively shaping agents via human reinforcement: The tamer framework
W Bradley Knox and Peter Stone · 2009
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Safe exploration of state and action spaces in reinforcement learning
Javier Garcia and Fernando Fernández · 2012
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Policy gradients with variance related risk criteria
Aviv Tamar, Dotan Di Castro, and Shie Mannor · 2012
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Reachability-based safe learning with gaussian processes
Anayo K Akametalu, Jaime F Fisac, Jeremy H Gillula, Shahab Kaynama, Melanie N Zeilinger, and Claire J Tomlin · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Optimized assistive human–robot interaction using reinforcement learning
Hamidreza Modares, Isura Ranatunga, Frank L Lewis, and Dan O Popa · 2015
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Safe exploration for optimization with gaussian processes
Yanan Sui, Alkis Gotovos, Joel Burdick, and Andreas Krause · 2015
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Safe learning of regions of attraction for uncertain, nonlinear systems with gaussian processes
Felix Berkenkamp, Riccardo Moriconi, Angela P Schoellig, and Andreas Krause · 2016
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Modelplex: Verified runtime validation of verified cyber-physical system models
Stefan Mitsch and André Platzer · 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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Improving robot controller transparency through autonomous policy explanation
Bradley Hayes and Julie A Shah · 2017
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Interactive learning from policy-dependent human feedback
James MacGlashan, Mark K Ho, Robert Loftin, Bei Peng, Guan Wang, David L Roberts, Matthew E Taylor, and Michael L Littman · 2017
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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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Supervising strong learners by amplifying weak experts
Paul Christiano, Buck Shlegeris, and Dario Amodei · 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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Learning-based model predictive control for safe exploration
Torsten Koller, Felix Berkenkamp, Matteo Turchetta, and Andreas Krause · 2018
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Human interactive behavior: A bibliographic review
Xiangjie Kong, Kai Ma, Shen Hou, Di Shang, and Feng Xia · 2018
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Learning dynamic robot-to-human object handover from human feedback
Andras Kupcsik, David Hsu, and Wee Sun Lee · 2018
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Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg · 2018
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Trial without error: Towards safe reinforcement learning via human intervention
William Saunders, Girish Sastry, Andreas Stuhlmüller, and Owain Evans · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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Stagewise safe bayesian optimization with gaussian processes
Yanan Sui, Vincent Zhuang, Joel Burdick, and Yisong Yue · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Contrastive explanations for reinforcement learning in terms of expected consequences
J van der Waa, J van Diggelen, K van den Bosch, and M Neerincx · 2018
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Safe exploration and optimization of constrained mdps using gaussian processes
Akifumi Wachi, Yanan Sui, Yisong Yue, and Masahiro Ono · 2018
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On the utility of learning about humans for human-ai coordination
Micah Carroll, Rohin Shah, Mark K Ho, Tom Griffiths, Sanjit Seshia, Pieter Abbeel, and Anca Dragan · 2019
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Lyapunov-based safe policy optimization for continuous control
Yinlam Chow, Ofir Nachum, Aleksandra Faust, Edgar Duenez-Guzman, and Mohammad Ghavamzadeh · 2019
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Liir: learning individual intrinsic reward in multi-agent reinforcement learning
Yali Du, Lei Han, Meng Fang, Tianhong Dai, Ji Liu, and Dacheng Tao · 2019
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Adversarial training is not ready for robot learning
Mathias Lechner, Ramin Hasani, Radu Grosu, Daniela Rus, and Thomas A Henzinger · 2021
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Robot learning with crash constraints
Alonso Marco, Dominik Baumann, Majid Khadiv, Philipp Hennig, Ludovic Righetti, and Sebastian Trimpe · 2021
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Safe reinforcement learning: A control barrier function optimization approach
Zahra Marvi and Bahare Kiumarsi · 2021
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Toward robots’ behavioral transparency of temporal difference reinforcement learning with a human teacher
Marco Matarese, Alessandra Sciutti, Francesco Rea, and Silvia Rossi · 2021
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Collaborating with humans without human data
DJ Strouse, Kevin McKee, Matt Botvinick, Edward Hughes, and Richard Everett · 2021
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Safety and Robustness in Reinforcement Learning
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Christopher Frye and Ilya Feige · 2019
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Grid-wise control for multi-agent reinforcement learning in video game ai
Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, and Tong Zhang · 2019
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Temporal logic guided safe reinforcement learning using control barrier functions
Xiao Li and Calin Belta · 2019
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Safe exploration for interactive machine learning
Matteo Turchetta, Felix Berkenkamp, and Andreas Krause · 2019
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A survey on interactive reinforcement learning: Design principles and open challenges
Christian Arzate Cruz and Takeo Igarashi · 2020
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Human-centered collaborative robots with deep reinforcement learning
Ali Ghadirzadeh, Xi Chen, Wenjie Yin, Zhengrong Yi, Mårten Björkman, and Danica Kragic · 2020
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Safe reinforcement learning for autonomous lane changing using set-based prediction
Hanna Krasowski, Xiao Wang, and Matthias Althoff · 2020
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Matteo Turchetta · 2021
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Safety robustness of reinforcement learning policies: A view from robust control
Hao Xiong and Xiumin Diao · 2021
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Dear: Deep reinforcement learning for online advertising impression in recommender systems
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Learning end-to-end 6dof grasp choice of human-to-robot handover using affordance prediction and deep reinforcement learning
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Scaling laws for reward model overoptimization
Leo Gao, John Schulman, and Jacob Hilton · 2022
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Constrained reinforcement learning for vehicle motion planning with topological reachability analysis
Shangding Gu, Guang Chen, Lijun Zhang, Jing Hou, Yingbai Hu, and Alois Knoll · 2022
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A review of safe reinforcement learning: Methods, theory and applications
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Safeapt: Safe simulation-to-real robot learning using diverse policies learned in simulation
Rituraj Kaushik, Karol Arndt, and Ville Kyrki · 2022
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How to train your agent: Active learning from human preferences and justifications in safety-critical environments
Ilias Kazantzidis, Timothy J Norman, Yali Du, and Christopher T Freeman · 2022
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Niklas Kochdumper, Hanna Krasowski, Xiao Wang, Stanley Bak, and Matthias Althoff · 2022
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Provably safe reinforcement learning: A theoretical and experimental comparison
Hanna Krasowski, Jakob Thumm, Marlon Müller, Xiao Wang, and Matthias Althoff · 2022
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Towards safe, realistic testbed for robotic systems with human interaction
Bhoram Lee, Jonathan Brookshire, Rhys Yahata, and Supun Samarasekera · 2022
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Robot reinforcement learning on the constraint manifold
Puze Liu, Davide Tateo, Haitham Bou Ammar, and Jan Peters · 2022
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Meta-reward-net: Implicitly differentiable reward learning for preference-based reinforcement learning
Runze Liu, Fengshuo Bai, Yali Du, and Yaodong Yang · 2022
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On the robustness of safe reinforcement learning under observational perturbations
Zuxin Liu, Zijian Guo, Zhepeng Cen, Huan Zhang, Jie Tan, Bo Li, and Ding Zhao · 2022
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Integrating safety constraints into adversarial training for robust deep reinforcement learning
Jinling Meng, Fei Zhu, Yangyang Ge, and Peiyao Zhao · 2022
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Safe model-based reinforcement learning with an uncertainty-aware reachability certificate
Dongjie Yu, Wenjun Zou, Yujie Yang, Haitong Ma, Shengbo Eben Li, Jingliang Duan, and Jianyu Chen · 2022
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In situ bidirectional human-robot value alignment
Luyao Yuan, Xiaofeng Gao, Zilong Zheng, Mark Edmonds, Ying Nian Wu, Federico Rossano, Hongjing Lu, Yixin Zhu, and Song-Chun Zhu · 2022
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Safe multi-agent reinforcement learning for multi-robot control
Shangding Gu, Jakub Grudzien Kuba, Yuanpei Chen, Yali Du, Long Yang, Alois Knoll, and Yaodong Yang · 2023
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Pecan: Leveraging policy ensemble for context-aware zero-shot human-ai coordination
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Human–robot collaboration and machine learning: A systematic review of recent research
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