论文标题
DQN-AF：基于Q-Network的深度自适应转发策略，用于命名数据网络
DQN-AF: Deep Q-Network based Adaptive Forwarding Strategy for Named Data Networking
论文作者
论文摘要
由于出现了几个不可预见的设计缺陷，NDN引起了重大关注，这些缺陷在新的通信方案中变得显而易见。在其许多功能中，两种标准的NDN转发策略不是自适应的，在几种情况下会导致性能下降。本文提出了一种自适应转发策略，基于深厚的Q-NETWORK，基于深度强化学习，该策略分析了NDN路由器界面指标，而无需与NDN体系结构创建信号传导或损害设计原理，此外还显示出与标准策略相比的显着性能增长。
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.
