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Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself.
Consideration of risk in reinforcement learning
Matthias Heger · 1994
Earlier work this paper cites.
Constrained Markov decision processes , volume 7
Eitan Altman · 1999
Earlier work this paper cites.
Risk-sensitive reinforcement learning applied to control under constraints
Peter Geibel and Fritz Wysotzki · 2005
Earlier work this paper cites.
Path integrals and symmetry breaking for optimal control theory
Hilbert J Kappen · 2005
Earlier work this paper cites.
Reinforcement learning for mdps with constraints
Peter Geibel · 2006
Earlier work this paper cites.
Safe exploration for reinforcement learning
Alexander Hans, Daniel Schneegaß, Anton Maximilian Schäfer, and Steffen Udluft · 2008
Earlier work this paper cites.
Distributionally robust markov decision processes
Huan Xu and Shie Mannor · 2010
Earlier work this paper cites.
Modeling purposeful adaptive behavior with the principle of maximum causal entropy
Brian D Ziebart · 2010
Earlier work this paper cites.
Safe exploration in markov decision processes
Teodor Mihai Moldovan and Pieter Abbeel · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller · 2013
Earlier work this paper cites.
Algorithms for cvar optimization in mdps
Yinlam Chow and Mohammad Ghavamzadeh · 2014
Earlier work this paper cites.
Policy gradients beyond expectations: Conditional value-at-risk
Aviv Tamar, Yonatan Glassner, and Shie Mannor · 2014
Earlier work this paper cites.
A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 2015
Earlier work this paper cites.
End-to end training of deep visuomotor policies. arxiv preprint arxiv: 1504.00702
Sergey Levine, Chelsea Finn, Trevor Darrell, Pieter Abbeel, et al · 2015
Cited alongside, same era.
High-confidence off-policy evaluation
Philip S Thomas, Georgios Theocharous, and Mohammad Ghavamzadeh · 2015
Cited alongside, same era.
Combating reinforcement learning’s sisyphean curse with intrinsic fear
Zachary C Lipton, Kamyar Azizzadenesheli, Abhishek Kumar, Lihong Li, Jianfeng Gao, and Li Deng · 2016
Cited alongside, same era.
Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Risk-constrained reinforcement learning with percentile risk criteria
Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, and Marco Pavone · 2017
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Leave no trace: Learning to reset for safe and autonomous reinforcement learning
Soft actor-critic algorithms and applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al · 2018
Later among the works it cites.
Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
Later among the works it cites.
Reward constrained policy optimization
Chen Tessler, Daniel J. Mankowitz, and Shie Mannor · 2018
Later among the works it cites.
Lyapunov-based safe policy optimization for continuous control
Yinlam Chow, Ofir Nachum, Aleksandra Faust, Mohammad Ghavamzadeh, and Edgar A. Duéñez-Guzmán · 2019
Later among the works it cites.
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Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, and Sergey Levine · 2017
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Uncertainty-aware reinforcement learning for collision avoidance
Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, and Sergey Levine · 2017
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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Sergey Levine, Peter Pastor Sampedro, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen · 2017
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Robust adversarial reinforcement learning
Lerrel Pinto, James Davidson, Rahul Sukthankar, and Abhinav Gupta · 2017
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Safe visual navigation via deep learning and novelty detection
Charles Richter and Nicholas Roy · 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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Policy certificates: Towards accountable reinforcement learning
Christoph Dann, Lihong Li, Wei Wei, and Emma Brunskill · 2018
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Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, and Chelsea Finn · 2019
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Bridging hamilton-jacobi safety analysis and reinforcement learning
Jaime F Fisac, Neil F Lugovoy, Vicenç Rubies-Royo, Shromona Ghosh, and Claire J Tomlin · 2019
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Verifying aircraft collision avoidance neural networks through linear approximations of safe regions
Kyle D Julian, Shivam Sharma, Jean-Baptiste Jeannin, and Mykel J Kochenderfer · 2019
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Deep dynamics models for learning dexterous manipulation
Anusha Nagabandi, Kurt Konoglie, Sergey Levine, and Vikash Kumar · 2019
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Distributionally robust reinforcement learning
Elena Smirnova, Elvis Dohmatob, and Jérémie Mary · 2019
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Yichuan Charlie Tang, Jian Zhang, and Ruslan Salakhutdinov · 2019
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Distributional soft actor-critic: Off-policy reinforcement learning for addressing value estimation errors
Jingliang Duan, Yang Guan, Shengbo Eben Li, Yangang Ren, and Bo Cheng · 2020
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Distributional soft actor critic for risk sensitive learning
Xiaoteng Ma, Qiyuan Zhang, Li Xia, Zhengyuan Zhou, Jun Yang, and Qianchuan Zhao · 2020
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