The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning

Understand

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.