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From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility
Recent developments in machine-learning algorithms have led to impressiv...
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Hyperspectral Image Classification Based on Adaptive Sparse Deep Network
Sparse model is widely used in hyperspectral image classification.Howeve...
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Application of Deep Q-Network in Portfolio Management
Machine Learning algorithms and Neural Networks are widely applied to ma...
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Qualitative Measurements of Policy Discrepancy for Return-based Deep Q-Network
In this paper, we focus on policy discrepancy in return-based deep Q-net...
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Deep Reinforcement Learning based Adaptive Moving Target Defense
Moving target defense (MTD) is a proactive defense approach that aims to...
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Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking
Machine learning models have widely been used in fraud detection systems...
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A Frequency And Phase Attention Based Deep Learning Framework For Partial Discharge Detection On Insulated Overhead Conductors
Partial discharges are known as indicators of degradation of insulation ...
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DQN-AF: Deep Q-Network based Adaptive Forwarding Strategy for Named Data Networking
NDN has gained significant attention due to the appearance of several unforeseen design flaws that became evident with new communication scenarios. Among its many features, the two standard NDN forwarding strategies are not adaptive, causing performance degradation in several scenarios. This paper proposes an adaptive forwarding strategy based on deep reinforcement learning with Deep Q-Network, which analyzes the NDN router interface metrics without creating signaling overhead or harming the design principles from the NDN architecture, besides showing significant performance gains compared to the standard strategies.
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