Fine Tuning of Interval Configuration for Deep Reinforcement Learning Based Congestion Control
Haidlir Naqvi,
Muhammad Hafizhuddin Hilman,
Bayu Anggorojati
Abstract:It is apparent that various internet services in today’s digital ecosystem effectuate different types of networks’ quality of services (QoS) requirements. This condition, in fact, adds another level of complexity to the current network congestion control protocols. Therefore, it drives the adoption of deep reinforcement learning to improve the protocols’ adaptability to the dynamic networks’ QoS requirements. In this case, the state-of-the-art works on congestion control protocols, formulate the markov decisio… Show more
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