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Spacecraft Rendezvous Guidance Method Based on Safe Reinforcement Learning [PDF]
Jisuanji kexue, 2023 With the increasing complexity of spacecraft rendezvous and docking tasks,the requirements for its efficiency,autonomy and reliability are highly demanded.In recent years,the introduction of reinforcement learning technology to solve the problem of ...
XING Linquan, XIAO Yingmin, YANG Zhibin, WEI Zhengmin, ZHOU Yong, GAO Saijun
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Safe reinforcement learning for high-speed autonomous racing
Cognitive Robotics, 2023 The conventional application of deep reinforcement learning (DRL) to autonomous racing requires the agent to crash during training, thus limiting training to simulation environments.
Benjamin D. Evans +2 more
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Action Constrained Deep Reinforcement Learning Based Safe Automatic Driving Method [PDF]
Jisuanji kexue, 2021 With the development of artificial intelligence,the field of autonomous driving is also growing.The deep reinforcement learning (DRL) method is one of the main research methods in this field.DRL algorithms have been reported to achieve excellent ...
DAI Shan-shan, LIU Quan
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Towards Human-Level Safe Reinforcement Learning in Atari Library
Jurnal Sisfokom, 2023 Reinforcement learning (RL) is a powerful tool for training agents to perform complex tasks. However, from time-to-time RL agents often learn to behave in unsafe or unintended ways.
Afriyadi Afriyadi, Wiranto Herry Utomo
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Deep Reinforcement Learning Task Assignment Based on Domain Knowledge
IEEE Access, 2022 Deep Reinforcement Learning (DRL) methods are inefficient in the initial strategy exploration process due to the huge state space and action space in large-scale complex scenarios.
Jiayi Liu +4 more
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Safe reinforcement learning for dynamical systems using barrier certificates
Connection Science, 2022 Safety control is a fundamental problem in policy design. Basic reinforcement learning is effective at learning policy with goal-reaching property. However, it does not guarantee safety property of the learned policy.
Qingye Zhao, Yi Zhang, Xuandong Li
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Safe Reinforcement Learning for Transition Control of Ducted-Fan UAVs
Drones, 2023 Ducted-fan tail-sitter unmanned aerial vehicles (UAVs) provide versatility and unique benefits, attracting significant attention in various applications.
Yanbo Fu, Wenjie Zhao, Liu Liu
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Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties
Frontiers in Robotics and AI, 2021 Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases.
S. M. Nahid Mahmud +3 more
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A Survey of Safe Reinforcement Learning Methods in Robotics [PDF]
ITM Web of ConferencesReinforcement learning has achieved remarkable results in complex decision-making tasks such as robot control, but its deployment in real-world environments still faces safety challenges.
Xie Yuen
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Safe deployment of a reinforcement learning robot using self stabilization
Intelligent Systems with Applications, 2022 In toy environments like video games, a reinforcement learning agent is deployed and operates within the same state space in which it was trained.
Nanda Kishore Sreenivas, Shrisha Rao
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