Results 41 to 50 of about 1,629,210 (315)
Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks [PDF]
, 2019 Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are ...
Kamiya, Shotaro +5 more
core +2 more sources
Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning
IEEE Access, 2019 Cloud Computing provides an effective platform for executing large-scale and complex workflow applications with a pay-as-you-go model. Nevertheless, various challenges, especially its optimal scheduling for multiple conflicting objectives, are yet to be ...
Yuandou Wang +7 more
semanticscholar +1 more source
AIP Advances, 2023 Meshing is a critical, but user-intensive process necessary for stable and accurate simulations in computational fluid dynamics (CFD). Mesh generation is often a bottleneck in CFD pipelines.
Cooper Lorsung, Amir Barati Farimani
semanticscholar +1 more source
Transmission Network Dynamic Planning Based on a Double Deep-Q Network With Deep ResNet
IEEE Access, 2021 Based on a Double Deep-Q Network with deep ResNet (DDQN-ResNet), this paper proposes a novel method for transmission network expansion planning (TNEP). Since TNEP is a large scale and mixed-integer linear programming (MILP) problem, as the transmission ...
Yuhong Wang +7 more
doaj +1 more source
, 2017 We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards. Recent approaches have shown great successes in many Atari 2600 domains. However, domains with long horizons and sparse rewards, such as Montezuma's Revenge and Venture, remain challenging for existing methods.
Roderick, Melrose +2 more
openaire +2 more sources
Computer systems science and engineering, 2023 Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation (5G) networks.
M. Ouamri +4 more
semanticscholar +1 more source
Cross DQN: Cross Deep Q Network for Ads Allocation in Feed [PDF]
The Web Conference, 2021 E-commerce platforms usually display a mixed list of ads and organic items in feed. One key problem is to allocate the limited slots in the feed to maximize the overall revenue as well as improve user experience, which requires a good model for user ...
G. Liao +7 more
semanticscholar +1 more source
, 2017 We propose a framework that directly tackles the probability distribution of the value function parameters in Deep Q Network (DQN), with powerful variational inference subroutines to approximate the posterior of the parameters. We will establish the equivalence between our proposed surrogate objective and variational inference loss.
Tang, Yunhao, Kucukelbir, Alp
openaire +2 more sources
An Exoatmospheric Homing Guidance Law Based on Deep Q Network
International Journal of Aerospace Engineering, 2022 A homing guidance law for exoatmospheric interceptor based on the Deep Q Network (DQN) algorithm is proposed in this paper. Aiming at the exoatmospheric interception problem, the guidance agent is built with the help of the deep reinforcement learning ...
Jin Tang +4 more
doaj +1 more source
Image2GIF: Generating Cinemagraphs Using Recurrent Deep Q-Networks [PDF]
2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018 WACV2018
Zhou, Yipin, Song, Yale, Berg, Tamara L.
openaire +2 more sources