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Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments.
Evolutionary principles in self-referential learning. on learning now to learn: The meta-meta-meta…-hook
Jurgen Schmidhuber · 1987
Earlier work this paper cites.
Meta-neural networks that learn by learning
Devang K Naik and Richard J Mammone · 1992
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Learning to learn: Introduction and overview
Sebastian Thrun and Lorien Pratt · 1998
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Constrained Markov Decision Processes
Eitan Altman · 1999
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Learning to learn using gradient descent
Sepp Hochreiter, A. Steven Younger, and Peter R. Conwell · 2001
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Risk-sensitive reinforcement learning applied to control under constraints
Fritz Wysotzki Peterr Geibel · 2005
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Conservative safety critics for exploration
Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, and Animesh Garg · 2010
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Conservative safety critics for exploration
Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, and Animesh Garg · 2010
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Hamilton-jacobi formulation for reach-avoid differential games
Kostas Margellos and John Lygeros · 2011
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Guaranteed safe online learning via reachability: tracking a ground target using a quadrotor
J. H. Gillula and C. J. Tomlin · 2012
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Ensuring safety of nonlinear sampled data systems through reachability
Meeko Oishi Ian M. Mitchell, Mo Chen · 2012
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Guaranteed infinite horizon avoidance of unpredictable, dynamically constrained obstacles
Albert Wu and Jonathan P. How · 2012
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Safe and robust learning control with gaussian processes
Felix Berkenkamp and Angela P. Schoellig · 2015
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Safe controller optimization for quadraotors with gaussian processes
Felix Berkenkamp, Angela P. Schoellig, and Andreas Krause · 2015
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Safe platooning of unmanned aerial vehicles via reachability
Mo Chen, Qie Hu, Casey Mackin, Jaime Fisac, and Claire Tomlin · 2015
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Risk-constrained reinforcement learning with percentile risk criteria
Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, and Marco Pavone · 2015
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A comprehensive survey on safe reinforcement learning
J. Garcia and F. Fernández · 2015
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Learning compound multi-step controllers under unknown dynamics
Weiqiao Han, Sergey Levine, and Pieter Abbeel · 2015
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Rl 2 : Fast reinforcement learning via slow reinforcement learning, 2016
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Hamilton-jacobi reachability: A brief overview and recent advances
Somil Bansal, Mo Chen, Sylvia Herbert, and Claire J. Tomlin · 2017
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Safe model-based reinforcement learning with stability guarantees
Felix Berkenkamp, Matteo Turchetta, Angela P. Schoellig, and Andreas Krause · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks, 2017
Meta reinforcement learning as task inference, 2019
Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, and Nicolas Heess · 2019
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning, 2019
Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables, 2019
Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, and Sergey Levine · 2019
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Reward constrained policy optimization
Chen Tessler, Daniel J. Mankowitz, and Shie Mannor · 2019
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Meta reinforcement learning for sim-to-real domain adaptation
Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, and Ville Kyrki · 2020
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Meta-q-learning, 2020
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Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Safe autonomy under perception uncertainty using chance-constrained temporal logic
Susmit Jha, Vasumathi Raman, Dorsa Sadigh, and Sanjit A. Seshia · 2017
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Learning to reinforcement learn, 2017
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 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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A lyapunov-based approach to safe reinforcement learning
Y. Chow, O. Nachum, E. Duéñez-Guzmán, and M. Ghavamzadeh · 2018
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Leave no trace: Learning to reset for safe and autonomous reinforcement learning
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, and Sergey Levine · 2018
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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 · 2018
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Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, and Alexander J. Smola · 2020
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Safe reinforcement learning through meta-learned instincts, 2020
Djordje Grbic and Sebastian Risi · 2020
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Robust model predictive shielding for safe reinforcement learning with stochastic dynamics
Shuo Li and Osbert Bastani · 2020
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Offline meta-reinforcement learning with advantage weighting, 2020
Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, and Chelsea Finn · 2020
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Rapidly adaptable legged robots via evolutionary meta-learning
Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, and Jie Tan · 2020
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Learning to be safe: Deep rl with a safety critic
Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha, Jie Tan, and Chelsea Finn · 2020
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Pytorch implementation of soft actor critic
Pranjal Tandon · 2020
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Offline meta learning of exploration, 2021
Ron Dorfman, Idan Shenfeld, and Aviv Tamar · 2021
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How to train your robot with deep reinforcement learning: Lessons we have learned
Julian Ibarz, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, and Sergey Levine · 2021
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Efficient fully-offline meta-reinforcement learning via distance metric learning and behavior regularization
Lanqing Li, Rui Yang, and Dijun Luo · 2021
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Decoupling exploration and exploitation for meta-reinforcement learning without sacrifices, 2021
Evan Zheran Liu, Aditi Raghunathan, Percy Liang, and Chelsea Finn · 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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Ls3: Latent space safe sets for long-horizon visuomotor control of iterative tasks
Albert Wilcox*, Ashwin Balakrishna*, Brijen Thananjeyan, Joseph E. Gonzalez, and Ken Goldberg · 2021
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