  Adaptivity, reconfigurability and intelligence are key features of the
next-generation wireless networks to meet the increasingly diverse quality of
service (QoS) requirements of the future applications. Conventional protocol
designs, however, struggle to provide flexibility and agility to changing radio
environments, traffic types and different user service requirements. In this
paper, we explore the potential of deep reinforcement learning (DRL), in
particular Proximal Policy Optimization (PPO), to design and configure
intelligent and application-specific medium access control (MAC) protocols. We
propose a framework that enables the addition, removal, or modification of
protocol features to meet individual application needs. The DRL channel access
policy design empowers the protocol to adapt and optimize in accordance with
the network and radio environment. Through extensive simulations, we
demonstrate the superior performance of the learned protocols over legacy IEEE
802.11ac in terms of throughput and latency.
