Results 171 to 180 of about 6,304,901 (198)
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2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2018
This paper highlights camera orientation estimation accuracy and precision, as well as proposing a new camera calibration technique using a reinforcement learning method named PPO (Proximal Policy Optimization) in offline mode. The offline mode is used just for extracting the camera geometry parameters that are used for improving accuracy in real-time ...
Haitham Al-Jabri, Takafumi Matsumaru
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This paper highlights camera orientation estimation accuracy and precision, as well as proposing a new camera calibration technique using a reinforcement learning method named PPO (Proximal Policy Optimization) in offline mode. The offline mode is used just for extracting the camera geometry parameters that are used for improving accuracy in real-time ...
Haitham Al-Jabri, Takafumi Matsumaru
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GAA-PPO: A novel graph adversarial attack method by incorporating proximal policy optimization
Neurocomputing, 2023Shuxin Yang +6 more
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Journal of The Korean Society of Living Environmental System, 2023
Dong-Kyu Lee, Hyeun-Jun Moon
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Dong-Kyu Lee, Hyeun-Jun Moon
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Journal of the Franklin Institute
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xin-Yu Xu +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xin-Yu Xu +2 more
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Proceedings of the AAAI Conference on Artificial Intelligence
This paper investigates the reliable parking space search problem in structured environments, with the objective of minimizing the linear combination of mean and standard deviation (mean-std) parking space search time. While canonical parking space search algorithms usually target the minimal expected search time, we argue that risk-averse users would ...
Haoming Chen, Hongliang Guo
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This paper investigates the reliable parking space search problem in structured environments, with the objective of minimizing the linear combination of mean and standard deviation (mean-std) parking space search time. While canonical parking space search algorithms usually target the minimal expected search time, we argue that risk-averse users would ...
Haoming Chen, Hongliang Guo
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2023
W artykule dokonano szczegółowej analizy zagadnienia uczenia przez wzmacnianie (ang. Reinforcement Learning, RL) oraz jego praktycznych zastosowań, koncentrując się na kluczowych aspektach tego podejścia. Omówiono skuteczność algorytmów RL oraz wyzwania, z którymi się one zmagają. Następnie przedstawiono zrozumiałe wyjaśnienie mechanizmu działania tych
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W artykule dokonano szczegółowej analizy zagadnienia uczenia przez wzmacnianie (ang. Reinforcement Learning, RL) oraz jego praktycznych zastosowań, koncentrując się na kluczowych aspektach tego podejścia. Omówiono skuteczność algorytmów RL oraz wyzwania, z którymi się one zmagają. Następnie przedstawiono zrozumiałe wyjaśnienie mechanizmu działania tych
openaire +1 more source
Authentic Boundary Proximal Policy Optimization
IEEE Transactions on Cybernetics, 2022Xuesong Wang, Yuhu Cheng, Longyang Huang
exaly
Proximal Policy Optimization With Policy Feedback
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022Philip N L Chen +2 more
exaly
Upper confident bound advantage function proximal policy optimization
Cluster Computing, 2022Zhi Hu
exaly
Proximal policy optimization via enhanced exploration efficiency
Information Sciences, 2022Shuai Lu, Zhenghao Zhang, Shuai Han
exaly