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Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning.
Safe policies for reinforcement learning via primal-dual methods
Santiago Paternain, Miguel Calvo-Fullana, Luiz FO Chamon, and Alejandro Ribeiro · 1911
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Eitan Altman · 1999
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Sham Kakade and John Langford · 2002
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Thomas Jaksch, Ronald Ortner, and Peter Auer · 2010
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Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin Riedmiller · 2012
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A comprehensive survey on safe reinforcement learning
Javier Garcıa and Fernando Fernández · 2015
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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 Schoellig, and Andreas Krause · 2017
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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 · 2017
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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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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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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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Learning-based model predictive control for safe exploration
Torsten Koller, Felix Berkenkamp, Matteo Turchetta, and Andreas Krause · 2018
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On infusing reachability-based safety assurance within probabilistic planning frameworks for human-robot vehicle interactions
Karen Leung, Edward Schmerling, Mo Chen, John Talbot, J Christian Gerdes, and Marco Pavone · 2018
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Deep reinforcement learning course, 2018
Sergey Levine · 2018
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Reward constrained policy optimization
Chen Tessler, Daniel J Mankowitz, and Shie Mannor · 2018
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Worst-case regret bounds for exploration via randomized value functions
Daniel Russo · 2019
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Samba: Safe model-based & active reinforcement learning
Alexander I Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Abdullah, Aivar Sootla, Jun Wang, and Haitham Ammar · 2020
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Safe reinforcement learning through meta-learned instincts
Djordje Grbic and Sebastian Risi · 2020
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Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Reachability-based safety guarantees using efficient initializations
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Benchmarking safe exploration in deep reinforcement learning
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Learning agile robotic locomotion skills by imitating animals
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