A New Fourier Q Operator Network Based Reinforcement Learning Method for Continuous Action Space Decision-making in Manufacturing
Tài liệu tham khảo
Li, 2019, AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms, Sci. Robot., 4, 10.1126/scirobotics.aaw0863
Li, 2023, A data and knowledge-driven cutting parameter adaptive optimization method considering dynamic tool wear, Robot. Comput. Integr. Manuf., 81, 10.1016/j.rcim.2022.102491
Ren, 2020, Medical Treatment Migration Prediction Based on GCN via Medical Insurance Data, IEEE J. Biomed. Heal. Informatics., 24, 2516, 10.1109/JBHI.2020.3008493
Ni, 2022, A mechanism informed neural network for predicting machining deformation of annular parts, Adv. Eng. Informatics., 53, 10.1016/j.aei.2022.101661
Wang, 2022, Design and development of a five-axis machine tool with high accuracy, stiffness and efficiency for aero-engine casing manufacturing, Chinese J. Aeronaut., 35, 485, 10.1016/j.cja.2021.04.001
Dong, 2022, Adaptability Control Towards Complex Ground Based on Fuzzy Logic for Humanoid Robots, IEEE Trans. Fuzzy Syst., 30, 1574, 10.1109/TFUZZ.2022.3167458
Mujica, 2023, Robust variable admittance control for human–robot co-manipulation of objects with unknown load, Robot. Comput. Integr. Manuf., 79, 10.1016/j.rcim.2022.102408
Li, 2021, Optimal Cost Minimization Strategy for Fuel Cell Hybrid Electric Vehicles Based on Decision-Making Framework, IEEE Trans. Ind. Informatics., 17, 2388, 10.1109/TII.2020.3003554
Wang, 2022, Joint energy consumption optimization method for wing-diesel engine-powered hybrid ships towards a more energy-efficient shipping, Energy, 245, 10.1016/j.energy.2022.123155
Stavropoulos, 2022, Infrared (IR) quality assessment of robotized resistance spot welding based on machine learning, Int. J. Adv. Manuf. Technol., 119, 1785, 10.1007/s00170-021-08320-8
Stavropoulos, 2023, Optimization of Milling Processes: Chatter Detection via a Sensor-Integrated Vice †, Machines, 11, 10.3390/machines11010052
Meghdadi, 2022, A Quantum-Like Model for Predicting Human Decisions in the Entangled Social Systems, IEEE Trans. Cybern., 52, 5778, 10.1109/TCYB.2021.3134688
Lu, 2022, Reward Shaping-Based Actor-Critic Deep Reinforcement Learning for Residential Energy Management, IEEE Trans. Ind. Informatics., 1
Huang, 2022, Graph neural network and multi-agent reinforcement learning for machine-process-system integrated control to optimize production yield, J. Manuf. Syst., 64, 81, 10.1016/j.jmsy.2022.05.018
Elguea-Aguinaco, 2023, A review on reinforcement learning for contact-rich robotic manipulation tasks, Robot. Comput. Integr. Manuf., 81, 10.1016/j.rcim.2022.102517
Jiang, 2022, Contour error modeling and compensation of CNC machining based on deep learning and reinforcement learning, Int. J. Adv. Manuf. Technol., 118, 551, 10.1007/s00170-021-07895-6
Mnih
Van Hasselt
Wang
Lillicrap
Haarnoja
Li, 2021, Maneuvering target tracking of UAV based on MN-DDPG and transfer learning, Def. Technol., 17, 457, 10.1016/j.dt.2020.11.014
Chen, 1995, Universal Approximation to Nonlinear Operators by Neural Networks with Arbitrary Activation Functions and Its Application to Dynamical Systems, IEEE Trans. Neural Networks., 6, 911, 10.1109/72.392253
X. Guo, W. Li, F. Iorio, Convolutional neural networks for steady flow approximation, Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. 13-17-August-2016 (2016) 481–490. https://doi.org/10.1145/2939672.2939738.
Adler, 2017, Solving ill-posed inverse problems using iterative deep neural networks, Inverse Probl, 33, 1, 10.1088/1361-6420/aa9581
Lu
Nelsen, 2021, The random feature model for input-output maps between banach spaces, SIAM J. Sci. Comput., 43, A3212, 10.1137/20M133957X
Patel, 2021, A physics-informed operator regression framework for extracting data-driven continuum models, Comput. Methods Appl. Mech. Eng., 373, 10.1016/j.cma.2020.113500
Li, 2021, Fourier neural operator for parametric partial differential equations, 38th Int. Conf. Mach. Learn.
G. Chen, Y. Li, X. liu, Q. Meng, J. Zhou, X. Hao, Residual fourier neural operator for thermochemical curing of composites, (2021). http://arxiv.org/abs/2111.10262.
Ge, 2018
Shoushen, 2018, Machining technology of large diameter thin wall aluminum casing, New Technol. New Prod. China, 4, 68
Zhao, 2022, A New Method for Inferencing and Representing a Workpiece Residual Stress Field Using Monitored Deformation Force Data, Engineering, 18
Hao, 2019, A part deformation control method via active pre-deformation based on online monitoring data, Int. J. Adv. Manuf. Technol., 104, 2681, 10.1007/s00170-019-04127-w
Zhang, 2020, A new in-processes active control method for reducing the residual stresses induced deformation of thin-walled parts, J. Manuf. Process., 59, 316, 10.1016/j.jmapro.2020.09.079
Zhou, 2016
Wang, 2014
Hester, 2018, Deep q-learning from demonstrations, 3223
Gulcehre
Badia, 2020, Never Give Up: Learning Directed Exploration Strategies,, Int. Conf. Learn. Represent, 1
Harutyunyan, 2019, Hindsight credit assignment, Adv. Neural Inf. Process. Syst., 32, 1
Y. Liu, Y. Luo, Y. Zhong, X. Chen, Q. Liu, J. Peng, Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning, (2019). http://arxiv.org/abs/1905.13420.
Finn
Liu, 2022, A meta-reinforcement learning method by incorporating simulation and real data for machining deformation control of finishing process, Int. J. Prod. Res.
Zhang, 2020, Robotic constant-force grinding control with a press-and-release model and model-based reinforcement learning, Int. J. Adv. Manuf. Technol., 106, 589, 10.1007/s00170-019-04614-0
Ding, 2023, Impedance control and parameter optimization of surface polishing robot based on reinforcement learning, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., 237, 216, 10.1177/09544054221100004