  In this paper, we apply an multi-agent reinforcement learning (MARL)
framework allowing the base station (BS) and the user equipments (UEs) to
jointly learn a channel access policy and its signaling in a wireless multiple
access scenario. In this framework, the BS and UEs are reinforcement learning
(RL) agents that need to cooperate in order to deliver data. The comparison
with a contention-free and a contention-based baselines shows that our
framework achieves a superior performance in terms of goodput even in high
traffic situations while maintaining a low collision rate. The scalability of
the proposed method is studied, since it is a major problem in MARL and this
paper provides the first results in order to address it.
