Results 11 to 20 of about 1,629,210 (315)

Double Deep Q-Network-Based Energy-Efficient Resource Allocation in Cloud Radio Access Network

IEEE Access, 2021
Cloud radio access network (CRAN) has been shown as an effective means to boost network performance. Such gain stems from the intelligent management of remote radio heads (RRHs) in terms of on/off operation mode and power consumption.
Amjad Iqbal, Mau-Luen Tham, Yoong Choon Chang   +2 more
doaj   +2 more sources

Multiple Unmanned Aerial Vehicle Autonomous Path Planning Algorithm Based on Whale-Inspired Deep Q-Network

Drones, 2023
In emergency rescue missions, rescue teams can use UAVs and efficient path planning strategies to provide flexible rescue services for trapped people, which can improve rescue efficiency and reduce personnel risks.
Wenshan Wang, Guoyin Zhang, Qingan Da, Dan Lu, Yingnan Zhao, Sizhao Li, Dapeng Lang   +6 more
doaj   +2 more sources

DM-DQN: Dueling Munchausen deep Q network for robot path planning

Complex & Intelligent Systems, 2022
In order to achieve collision-free path planning in complex environment, Munchausen deep Q-learning network (M-DQN) is applied to mobile robot to learn the best decision. On the basis of Soft-DQN, M-DQN adds the scaled log-policy to the immediate reward.
Yuwan Gu, Zhitao Zhu, Jidong Lv, Lin Shi, Zhenjie Hou, Shoukun Xu   +5 more
doaj   +2 more sources

Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management. [PDF]

Sci Rep
The growing integration of renewable energy sources within microgrids necessitates innovative approaches to optimize energy management. While microgrids offer advantages in energy distribution, reliability, efficiency, and sustainability, the variable ...
Ramesh S, N SB, Sathyavarapu SJ, Sharma V, A A NK, Khanna M.   +5 more
europepmc   +2 more sources

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications. [PDF]

Sensors (Basel), 2023
Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention.
Han D, So J.
europepmc   +2 more sources

An Enhanced Dueling Double Deep Q-Network With Convolutional Block Attention Module for Traffic Signal Optimization in Deep Reinforcement Learning

IEEE Access
Many studies on the application of deep reinforcement learning (DRL) in the field of traffic signal control do not fully consider the influence of vehicles approaching the intersection on traffic flow.
Peng Wang, Wenlong Ni
doaj   +2 more sources

Deep-Q-Network-Based Packet Scheduling in an IoT Environment. [PDF]

Sensors (Basel), 2023
With the advent of the Internet of Things (IoT) era, a wide array of wireless sensors supporting the IoT have proliferated. As key elements for enabling the IoT, wireless sensor nodes require minimal energy consumption and low device complexity.
Fu X, Kim JG.
europepmc   +2 more sources

Leader-follower UAVs formation control based on a deep Q-network collaborative framework. [PDF]

Sci Rep
This study examines a collaborative framework that utilizes an intelligent deep Q-network to regulate the formation of leader–follower Unmanned Aerial Vehicles (UAVs).
Liu Z, Li J, Shen J, Wang X, Chen P.
europepmc   +2 more sources

A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

IEEE Access, 2023
Distribution network reconfiguration (DNR) is one of the most important methods to cope with the increasing electricity demand due to the massive integration of electric vehicles.
Nastaran Gholizadeh, Nazli Kazemi, Petr Musilek   +2 more
doaj   +1 more source

Episodic Memory Deep Q-Networks [PDF]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requiring many rounds of interactions with the environments to obtain satisfactory performances.
Lin, Zichuan, Zhao, Tianqi, Yang, Guangwen, Zhang, Lintao   +3 more
openaire   +2 more sources