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A comprehensive survey on safe reinforcement learning
J. Mach. Learn. Res., 2015Summary: Safe Reinforcement Learning can be defined as the process of learning policies that maximize the expectation of the return in problems in which it is important to ensure reasonable system performance and/or respect safety constraints during the learning and/or deployment processes. We categorize and analyze two approaches of Safe Reinforcement
Javier García 0001 +1 more
openaire +2 more sources
IEEE Transactions on Information Forensics and Security, 2022
Xiaozhen Lu, Xiangyang Ji, Qian Wang
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Xiaozhen Lu, Xiangyang Ji, Qian Wang
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Safe Reinforcement Learning With Stability Guarantee for Motion Planning of Autonomous Vehicles
IEEE Transactions on Neural Networks and Learning Systems, 2021Ye Zhao, Minghao Han, Lixian Zhang
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Flooding mitigation through safe & trustworthy reinforcement learning
Journal of Hydrology, 2023Kunlun Xin, Zhenliang Liao, Muhan Zhao
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Safe deep reinforcement learning-based adaptive control for USV interception mission
Ocean Engineering, 2022Weidong Zhang, Bin Du, Chenming Zhang
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Safe Reinforcement Learning-based Driving Policy Design for Autonomous Vehicles on Highways
International Journal of Control, Automation and Systems, 2023Kyoungseok Han, Hung Duy Nguyen
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Learning-Based Model Predictive Control: Toward Safe Learning in Control
Annual Review of Control, Robotics, and Autonomous Systems, 2020Lukas Hewing +2 more
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Conflict-Aware Safe Reinforcement Learning: A Meta-Cognitive Learning Framework
IEEE/CAA Journal of Automatica Sinica, 2022Hamidreza Modares, Majid Mazouchi
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