  In this paper, we propose a new framework, exploiting the multi-agent deep
deterministic policy gradient (MADDPG) algorithm, to enable a base station (BS)
and user equipment (UE) to come up with a medium access control (MAC) protocol
in a multiple access scenario. In this framework, the BS and UEs are
reinforcement learning (RL) agents that need to learn to cooperate in order to
deliver data. The network nodes can exchange control messages to collaborate
and deliver data across the network, but without any prior agreement on the
meaning of the control messages. In such a framework, the agents have to learn
not only the channel access policy, but also the signaling policy. The
collaboration between agents is shown to be important, by comparing the
proposed algorithm to ablated versions where either the communication between
agents or the central critic is removed. The comparison with a contention-free
baseline shows that our framework achieves a superior performance in terms of
goodput and can effectively be used to learn a new protocol.
