  Networking protocols are designed through long-time and hard-work human
efforts. Machine Learning (ML)-based solutions have been developed for
communication protocol design to avoid manual efforts to tune individual
protocol parameters. While other proposed ML-based methods mainly focus on
tuning individual protocol parameters (e.g., adjusting contention window), our
main contribution is to propose a novel Deep Reinforcement Learning (DRL)-based
framework to systematically design and evaluate networking protocols. We
decouple a protocol into a set of parametric modules, each representing a main
protocol functionality that is used as DRL input to better understand the
generated protocols design optimization and analyze them in a systematic
fashion. As a case study, we introduce and evaluate DeepMAC a framework in
which a MAC protocol is decoupled into a set of blocks across popular flavors
of 802.11 WLANs (e.g., 802.11 b/a/g/n/ac). We are interested to see what blocks
are selected by DeepMAC across different networking scenarios and whether
DeepMAC is able to adapt to network dynamics.
