In this paper, we leverage a multi-agent reinforcement learning (MARL) framework to jointly learn a computation offloading decision and multichannel access policy with corresponding signaling. Specifically, the base station and industrial Internet of Things mobile devices are reinforcement learning agents that need to cooperate to execute their computation tasks within a deadline constraint. We adopt an emergent communication protocol learning framework to solve this problem. The numerical results illustrate the effectiveness of emergent communication in improving the channel access success rate and the number of successfully computed tasks compared to contention-based, contention-free, and no-communication approaches. Moreover, the proposed task offloading policy outperforms remote and local computation baselines.

翻译：本文利用多智能体强化学习（MARL）框架，联合学习计算卸载决策、多信道接入策略及其相应的信令方案。具体而言，基站与工业物联网移动设备作为强化学习智能体，需在截止时间约束下协作完成各自的计算任务。我们采用涌现通信协议学习框架来解决该问题。数值结果表明，与基于竞争、无竞争及无通信方法相比，涌现通信在提升信道接入成功率与成功计算任务数量方面具有显著有效性。此外，所提出的任务卸载策略优于远端和本地计算基准方案。