  To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6G
wireless networks, conventional medium access control (MAC) procedures need to
evolve to enable base stations (BSs) and user equipments (UEs) to automatically
learn innovative MAC protocols catering to extremely diverse services. This
topic has received significant attention, and several reinforcement learning
(RL) algorithms, in which BSs and UEs are cast as agents, are available with
the aim of learning a communication policy based on agents' local observations.
However, current approaches are typically overfitted to the environment they
are trained in, and lack robustness against unseen conditions, failing to
generalize in different environments. To overcome this problem, in this work,
instead of learning a policy in the high dimensional and redundant observation
space, we leverage the concept of observation abstraction (OA) rooted in
extracting useful information from the environment. This in turn allows
learning communication protocols that are more robust and with much better
generalization capabilities than current baselines. To learn the abstracted
information from observations, we propose an architecture based on autoencoder
(AE) and imbue it into a multi-agent proximal policy optimization (MAPPO)
framework. Simulation results corroborate the effectiveness of leveraging
abstraction when learning protocols by generalizing across environments, in
terms of number of UEs, number of data packets to transmit, and channel
conditions.
