Suchergebnisse - Advanced deep- Q network algorithm

Automated DoS Penetration Testing Using Deep Q Learning Network-Quantile Regression Deep Q Learning Network Algorithms von Alhamed, Mariam, Rahman, M M Hafizur

“… As a result of this algorithm, an attack tree is generated, paths within the attack graph are searched for, and a deep-first search method is used to create a transfer matrix …”
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Impact of energy storage devices on microgrid frequency performance: A robust DQN based grade-2 fuzzy cascaded controller von Sahu, Prakash Chandra, Jena, Smitasree, Mohapatra, Srikanta, Debdas, Subhra

“… •A microgrid is modeled by integrating various distributed power sources (DG) such as solar power stations (SPS), micro turbine (MT), wind power stations (WPS) …”
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Traffic prediction and load balancing routing algorithm based on deep Q-network for SD-IoT von Ding, Qiao, Li, Nanyu, Ding, Heng, Wang, Jian, Li, Tao, Chen, Yongqing, Xian, Yantuan, Chen, Junyang

“… ) networks are encountering increasingly severe challenges in routing and load balancing. To address this, this paper proposes an intelligent load-balancing routing algorithm based on deep Q-network (LBR-DQN …”
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An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the Internet of Things von Moghaddasi, Komeil, Rajabi, Shakiba, Gharehchopogh, Farhad Soleimanian, Ghaffari, Ali

“… ), an advanced Deep Reinforcement Learning (DRL) algorithm. This algorithm utilizes advanced neural networks to optimize task offloading in the three-tier framework …”
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Design of Big Data Task Scheduling Optimization Algorithm Based on Improved Deep Q-Network von Chen, Fu, Wu, Chunyi

“… To overcome this, a scheduling model based on Markov decision process is proposed. The deep Q-network algorithm is used for directed acyclic graph task scheduling …”
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Research on the Local Path Planning for Mobile Robots based on PRO-Dueling Deep Q-Network (DQN) Algorithm von Zhang, Yaoyu, Li, Caihong, Zhang, Guosheng, Zhou, Ruihong, Liang, Zhenying

“… This paper proposes a Pro-Dueling DQN algorithm to solve the problems of slow convergence speed and waste of effective experience of the traditional DQN (Deep Q-Network …”
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An empirical study of the naïve REINFORCE algorithm for predictive maintenance von Siraskar, Rajesh, Kumar, Satish, Patil, Shruti, Bongale, Arunkumar, Kotecha, Ketan, Kulkarni, Ambarish

“… However, these algorithms are extremely sensitive to hyperparameter tuning and network architecture, and this is where automated RL frameworks (AutoRL …”
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FAQ: A Fuzzy-Logic-Assisted Q Learning Model for Resource Allocation in 6G V2X von Zhang, Minglong, Dou, Yi, Marojevic, Vuk, Chong, Peter Han Joo, Chan, Henry C. B.

“… ) communications in the six generation (6G) cellular networks. Cellular V2X (C-V2X) communications empower advanced applications but at the same time bring unprecedented …”
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Simulating multi‐exit evacuation using deep reinforcement learning von Xu, Dong, Huang, Xiao, Mango, Joseph, Li, Xiang, Li, Zhenlong

“… MultiExit‐DRL, which involves a deep neural network (DNN) framework to facilitate state‐to‐action mapping. The DNN framework applies Rainbow Deep Q …”
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Two-Stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach von Wang, Xinan, Wang, Yishen, Shi, Di, Wang, Jianhui, Wang, Zhiwei

“… Enabled by the wide deployment of PMUs and advanced deep learning algorithms, proposed here is a double deep Q-learning network (DDQN …”
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Waste Objects Segregation Using Deep Reinforcement Learning with Deep Q Networks von Khan, Nida, Kulkarni, Kunal, Mahale, Yashashree, Kolhar, Shrikrishna, Mahajan, Smita

“… The Deep Q Network model proposed achieved an accuracy of approximately 73%. By employing DQN, an advanced reinforcement learning algorithm, the system ensures improved …”
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Fast or Slow: An Autonomous Speed Control Approach for UAV-assisted IoT Data Collection Networks von Chu, Nam H., Hoang, Dinh Thai, Nguyen, Diep N., Huynh, Nguyen Van, Dutkiewicz, Eryk

“… In this way, the Q-learning algorithm can be adopted to obtain the optimal speed control policy for the UAV …”
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Deep reinforcement learning for photovoltaic performance ratio estimation and accurate degradation stage diagnosis based on precise RS-RP evaluation von Salehpour, Sherko, Eskandari, Aref, Nedaei, Amir, Aghaei, Mohammadreza

“… ) algorithms, namely Deep Q-Network (DQN), and soft actor-critic (SAC), to (i) detect and distinguish degradation conditions using a binary classification model and (ii …”
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An exploratory study on intelligent active cell balancing of electric vehicle battery management and performance using machine learning algorithms von Rao, Veeranki Srinivasa, Sajja, Guna Sekhar, Manur, Vishwaraj B, Arandhakar, Sairaj, Krishna, V.B. Murali

“… •PA-RNN, Deep-Q, AQN, ADNN & AC enhance SoC accuracy and control.•Automotive Controllers are EV-specific for improved performance …”
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Optimization of cache-enabled opportunistic interference alignment wireless networks: A big data deep reinforcement learning approach von Ying He, Chengchao Liang, Yu, F. Richard, Nan Zhao, Hongxi Yin

“… Deep reinforcement learning is an advanced reinforcement learning algorithm that uses deep Q network to approximate the Q value-action function …”
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Discrete Space Deep Reinforcement Learning Algorithm Based on Support Vector Machine Recursive Feature Elimination von Kim, Chayoung

“… ) developed from the double deep Q-network (DDQN) in deep reinforcement learning (DRL) has become a standard …”
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Multi-Channel Opportunistic Access for Heterogeneous Networks Based on Deep Reinforcement Learning von Ye, Xiaowen, Yu, Yiding, Fu, Liqun

“… This paper investigates a new medium access control (MAC) protocol for multi-channel heterogeneous networks (HetNets …”
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Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks von Ying He, Zheng Zhang, Yu, F. Richard, Nan Zhao, Hongxi Yin, Leung, Victor C. M.

“… , which is an advanced reinforcement learning algorithm that uses a deep Q network to approximate the Q value-action function …”
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Advanced EEG Signal Processing and Deep Q-Learning for Accurate Student Attention Monitoring von Ur Rehman, Asad, Ul Eman, Noor, Ullah, Farhan, Cacciagrano, Diletta, Mostarda, Leonardo, Raza, Nabeel

“… This study introduces an enhanced approach that leverages advanced data preprocessing techniques and a Double Deep Q-Network (DDQN …”
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EdDSA Based Network Data Authentication and Intrusion Detection System Using QDQN‐DPER With Hippopotamus Optimization von Babu, B. V. Satish, Bikku, Thulasi, Ponkumar, D. David Neels, Uma Maheswaran, S. K., Maguluri, Lakshmana Phaneendra

“… ‐curve digital signature algorithm (EdDSA) and quantum deep Q‐learning with distributed prioritized experience replay (QDQN‐DPER …”
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