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NDN has gained significant attention due to the appearance of severalunforeseen design flaws that became evident with new communication scenarios.Among its many features, the two standard NDN forwarding strategies are notadaptive, causing performance degradation in several scenarios. This paperproposes an adaptive forwarding strategy based on deep reinforcement learningwith Deep Q-Network, which analyzes the NDN router interface metrics withoutcreating signaling overhead or harming the design principles from the NDNarchitecture, besides showing significant performance gains compared to thestandard strategies.

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