DQN-AF: Deep Q-Network based Adaptive Forwarding Strategy for Named Data Networking

10/19/2020 ∙ by Ygor Amaral B. L. de Sena, et al. ∙ 0 ∙ share

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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DQN-AF: Deep Q-Network based Adaptive Forwarding Strategy for Named Data Networking

Authors

From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility

Hyperspectral Image Classification Based on Adaptive Sparse Deep Network

Application of Deep Q-Network in Portfolio Management

Qualitative Measurements of Policy Discrepancy for Return-based Deep Q-Network

Deep Reinforcement Learning based Adaptive Moving Target Defense

Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking

A Frequency And Phase Attention Based Deep Learning Framework For Partial Discharge Detection On Insulated Overhead Conductors

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