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Deep Q-network-based traffic signal control models. [PDF]
PLoS ONE, 2021 Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic ...
Sangmin Park +4 more
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Stochastic Double Deep Q-Network [PDF]
IEEE Access, 2019 Estimation bias seriously affects the performance of reinforcement learning algorithms. The maximum operation may result in overestimation, while the double estimator operation often leads to underestimation.
Pingli Lv +3 more
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Path planning of mobile robot based on improved double deep Q-network algorithm [PDF]
Frontiers in NeuroroboticsAiming at the problems of slow network convergence, poor reward convergence stability, and low path planning efficiency of traditional deep reinforcement learning algorithms, this paper proposes a BiLSTM-D3QN (Bidirectional Long and Short-Term Memory ...
Zhenggang Wang +2 more
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Deep Q-network to produce polarization-independent perfect solar absorbers: a statistical report [PDF]
Nano Convergence, 2020 Using reinforcement learning, a deep Q-network was used to design polarization-independent, perfect solar absorbers. The deep Q-network selected the geometrical properties and materials of a symmetric three-layer metamaterial made up of circular rods on ...
Iman Sajedian +3 more
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A pushing-grasping collaborative method based on deep Q-network algorithm in dual viewpoints [PDF]
Scientific Reports, 2022 In the field of intelligent manufacturing, robot grasping and sorting is important content. However, there are some disadvantages in the traditional single-view-based manipulator grasping methods by using a 2D camera, where the efficiency and the ...
Gang Peng +4 more
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Deep Reinforcement Learning. Studiu de caz: Deep Q-Network [PDF]
Revista Română de Informatică și Automatică, 2019 Artificial Intelligence (AI) became today perhaps the most up-to-date topic in many areas. One of the main goals of AI is to create completely autonomous agents able to interact with the surrounding world and learn by trial and error optimal behaviors ...
Mihnea Horia VREJOIU
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Dynamic Jobshop Scheduling Algorithm Based on Deep Q Network
IEEE Access, 2021 Jobshop scheduling is a classic instance in the field of production scheduling. Solving and optimizing the scheduling problem of the jobshop can greatly reduce the production cost of the workshop and improve the processing efficiency, thereby improving ...
Yejian Zhao +4 more
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IET Smart Grid, 2022 Residential buildings are large consumers of energy. They contribute significantly to the demand placed on the grid, particularly during hours of peak demand.
Junlin Lu, Patrick Mannion, Karl Mason
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BMC Medical Imaging, 2022 Background Supervised deep learning in radiology suffers from notorious inherent limitations: 1) It requires large, hand-annotated data sets; (2) It is non-generalizable; and (3) It lacks explainability and intuition.
J. N. Stember, H. Shalu
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Deep Reinforcement Learning for Intelligent Penetration Testing Path Design
Applied Sciences, 2023 Penetration testing is an important method to evaluate the security degree of a network system. The importance of penetration testing attack path planning lies in its ability to simulate attacker behavior, identify vulnerabilities, reduce potential ...
Junkai Yi, Xiaoyan Liu
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