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Safe reinforcement learning is a promising path toward applying reinforcement learning algorithms to real-world problems, where suboptimal behaviors may lead to actual negative consequences.
Combating the compounding-error problem with a multi-step model
Kavosh Asadi, Dipendra Misra, Seungchan Kim, and Michael L. Littman · 1905
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Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
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Safe exploration for reinforcement learning
Alexander Hans, Daniel Schneegaß, Anton Maximilian Schäfer, and Steffen Udluft · 2008
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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 · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
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Deep reinforcement learning for robotic manipulation
Shixiang Gu, Ethan Holly, Timothy P. Lillicrap, and Sergey Levine · 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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Learning from the hindsight plan — episodic mpc improvement
Aviv Tamar, Garrett Thomas, Tianhao Zhang, Sergey Levine, and Pieter Abbeel · 2017
Cited alongside, same era.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 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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Leave no trace: Learning to reset for safe and autonomous reinforcement learning
B Eysenbach, S Gu, J Ibarz, and S Levine · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
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Exploring model-based planning with policy networks
Tingwu Wang and Jimmy Ba · 2019
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Autonomous navigation of stratospheric balloons using reinforcement learning
Marc G. Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C. Machado, Subhodeep Moitra, Sameera S. Ponda, and Ziyu Wang · 2020
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Conservative safety critics for exploration
Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, and Animesh Garg · 2020
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On the expressivity of neural networks for deep reinforcement learning
Kefan Dong, Yuping Luo, Tianhe Yu, Chelsea Finn, and Tengyu Ma · 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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Model-ensemble trust-region policy optimization
Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, and Pieter Abbeel · 2018
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Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees
Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, and Tengyu Ma · 2018
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Reward constrained policy optimization
Chen Tessler, Daniel J Mankowitz, and Shie Mannor · 2018
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
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Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees
Yuping Luo, Huazhe Xu, Yuanzhi Li, Yuandong Tian, Trevor Darrell, and Tengyu Ma · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine
Cited in the paper.
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Responsive safety in reinforcement learning by pid lagrangian methods
Adam Stooke, Joshua Achiam, and Pieter Abbeel · 2020
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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 · 2020
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Safe reinforcement learning via curriculum induction
Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, and Alekh Agarwal · 2020
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Accelerating safe reinforcement learning with constraint-mismatched policies
Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, and Peter J Ramadge · 2020
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Provably safe pac-mdp exploration using analogies
Melrose Roderick, Vaishnavh Nagarajan, and Zico Kolter · 2021
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Safe continuous control with constrained model-based policy optimization
Moritz A Zanger, Karam Daaboul, and J Marius Zöllner · 2021
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