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AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns.
Constrained Markov decision processes: stochastic modeling
Eitan Altman · 1999
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Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David McAllester, Satinder Singh, and Yishay Mansour · 1999
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A natural policy gradient
Sham M Kakade · 2001
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Increasing robotic wheelchair safety with collaborative control: Evidence from secondary task experiments
Tom Carlson and Yiannis Demiris · 2010
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Constrained model-based reinforcement learning with robust cross-entropy method
Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, and Ding Zhao · 2010
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Continuous control with deep reinforcement learning
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Trust region policy optimization
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
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Risk-constrained reinforcement learning with percentile risk criteria
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 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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Reward constrained policy optimization
Chen Tessler, Daniel J Mankowitz, and Shie Mannor · 2018
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Constrained cross-entropy method for safe reinforcement learning
Min Wen and Ufuk Topcu · 2018
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Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
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Benchmarking safe exploration in deep reinforcement learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
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Safe exploration by solving early terminated mdp
Hao Sun, Ziping Xu, Meng Fang, Zhenghao Peng, Jiadong Guo, Bo Dai, and Bolei Zhou · 2021
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Safe reinforcement learning by imagining the near future
Garrett Thomas, Yuping Luo, and Tengyu Ma · 2021
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Crpo: A new approach for safe reinforcement learning with convergence guarantee
Tengyu Xu, Yingbin Liang, and Guanghui Lan · 2021
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Constrained policy optimization via bayesian world models
Yarden As, Ilnura Usmanova, Sebastian Curi, and Andreas Krause · 2022
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Model-based safe deep reinforcement learning via a constrained proximal policy optimization algorithm
Ashish K Jayant and Shalabh Bhatnagar · 2022
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Learning off-policy with online planning
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Tianshou: A highly modularized deep reinforcement learning library
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Constrained update projection approach to safe policy optimization
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Penalized proximal policy optimization for safe reinforcement learning
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Dense reinforcement learning for safety validation of autonomous vehicles
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