Results 31 to 40 of about 199,848 (147)

OPTIMIZING QOS IN SELF ORGANIZING HETEROGENEOUS WIRELESS CELLULAR NETWORK USING FIREFLY ALGORITHM

ICTACT Journal on Communication Technology, 2022
Capacity and energy efficiency are crucial for next-generation wireless networks. Due to the dense deployment of base stations (BSs) in a heterogeneous network (HetNets), the consumption is from 60% to 80% of the total energy causing accentuated costs ...
Gajanan Uttam Patil, Girish Ashok Kulkarni   +1 more
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A Method of Optimizing Weight Allocation in Data Integration Based on Q-Learning for Drug-Target Interaction Prediction

Frontiers in Cell and Developmental Biology, 2022
Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing heterogeneous information, such as drug chemical structure
Jiacheng Sun, Jiacheng Sun, Jiacheng Sun, You Lu, You Lu, You Lu, Linqian Cui, Linqian Cui, Linqian Cui, Qiming Fu, Qiming Fu, Qiming Fu, Hongjie Wu, Jianping Chen, Jianping Chen   +14 more
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Regularized Q-Learning

Advances in Neural Information Processing Systems 37
Q-learning is widely used algorithm in reinforcement learning community. Under the lookup table setting, its convergence is well established. However, its behavior is known to be unstable with the linear function approximation case. This paper develops a new Q-learning algorithm that converges when linear function approximation is used.
Lim, Han-Dong, Lee, Donghwan
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Uncertainty-aware Path Planning using Reinforcement Learning and Deep Learning Methods [PDF]

Computer and Knowledge Engineering, 2020
This paper proposes new algorithms to improve Reinforcement Learning (RL) and Deep Q-Network (DQN) methods for path planning considering uncertainty in the perception of environment.
Nematollah Ab azar, Aref Shahmansoorian, Mohsen Davoudi   +2 more
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Successive Over-Relaxation ${Q}$ -Learning [PDF]

IEEE Control Systems Letters, 2020
In a discounted reward Markov Decision Process (MDP), the objective is to find the optimal value function, i.e., the value function corresponding to an optimal policy. This problem reduces to solving a functional equation known as the Bellman equation and a fixed point iteration scheme known as the value iteration is utilized to obtain the solution. In
Chandramouli Kamanchi, Raghuram Bharadwaj Diddigi, Shalabh Bhatnagar   +2 more
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Ramp Metering Control Based on the Q-Learning Algorithm

Cybernetics and Information Technologies, 2015
Modern urban highways are under the influence of increased traffic demand and cannot fulfill the desired level of service anymore. In most of the cases there is no space available for any infrastructure building.
Ivanjko Edouard, Koltovska Nečoska Daniela, Gregurić Martin, Vujić Miroslav, Jurković Goran, Mandžuka Sadko   +5 more
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Contextual Q-Learning

, 2020
This work has received funding from the EU Horizon 2020 research and innovation program under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under projects CEECIND/01811/2017 and UIDB/00760 ...
Vale, Zita, Pinto, Tiago
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Deep functional measurements of Fragile X syndrome human neurons reveal multiparametric electrophysiological disease phenotype

Communications Biology
Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by hypermethylation of expanded CGG repeats (>200) in the FMR1 gene leading to gene silencing and loss of Fragile X Messenger Ribonucleoprotein (FMRP) expression. FMRP plays important roles
James J. Fink, Nathaniel Delaney-Busch, Ryan Dawes, Evanthia Nanou, Christopher Folts, Karthiayani Harikrishnan, Chris Hempel, Hansini Upadhyay, Trinh Nguyen, Himali Shroff, David Stoppel, Steven J. Ryan, Jane Jacques, Jennifer Grooms, Elizabeth Berry-Kravis, Mark F. Bear, Luis A. Williams, David Gerber, Mark Bunnage, Brinley Furey, Graham T. Dempsey   +20 more
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Complexification through gradual involvement and reward Providing in deep reinforcement learning

Системный анализ и прикладная информатика
Training a relatively big neural network within the framework of deep reinforcement learning that has enough capacity for complex tasks is challenging. In real life the process of task solving requires system of knowledge, where more complex skills are ...
E. V. Rulko,
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Offloading decision algorithm based on reinforcement learning for mobile edge computing

Dianzi Jishu Yingyong, 2021
For the problem of computing offloading decision in mobile edge computing, this paper proposes an offloading decision algorithm based on enhanced learning in multiuser MEC system.
Yang Ge, Zhang Heng
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