Offloading computation to nearby edge/fog computing nodes, including the ones carried by moving vehicles, e.g., vehicular fog nodes (VFN), has proved to be a promising approach for enabling low-latency and compute-intensive mobility applications, such as cooperative and autonomous driving. This work considers vehicular fog computing scenarios where the clients of computation offloading services try to minimize their own costs while deciding which VFNs to offload their tasks. We focus on decentralized multi-agent decision-making in a repeated unknown game where each agent, e.g., service client, can observe only its own action and realized cost. In other words, each agent is unaware of the game composition or even the existence of opponents. We apply a completely uncoupled learning rule to generalize the decentralized decision-making algorithm presented in \cite{Cho2021} for the multi-agent case. The multi-agent solution proposed in this work can capture the unknown offloading cost variations susceptive to resource congestion under an adversarial framework where each agent may take implicit cost estimation and suitable resource choice adapting to the dynamics associated with volatile supply and demand. According to the evaluation via simulation, this work reveals that such individual perturbations for robustness to uncertainty and adaptation to dynamicity ensure a certain level of optimality in terms of social welfare, e.g., converging the actual sequence of play with unknown and asymmetric attributes and lowering the correspondent cost in social welfare due to the self-interested behaviors of agents.

翻译：将计算任务卸载至邻近的边缘/雾计算节点（包括移动车辆搭载的车载雾节点（VFN））已被证明是支撑低延迟、高计算需求移动应用（如协同与自动驾驶）的有效方法。本文考虑车载雾计算场景：计算卸载服务的客户端在决定将任务卸载至哪个VFN时，需最小化自身成本。我们聚焦于重复未知博弈中的去中心化多智能体决策问题，其中每个智能体（即服务客户端）仅能观测自身动作及其实际成本。换言之，每个智能体对博弈构成甚至对手的存在均一无所知。我们采用完全非耦合学习规则，将文献\cite{Cho2021}提出的去中心化决策算法推广至多智能体场景。本文提出的多智能体解决方案能够在对抗性框架下捕捉资源拥塞导致的未知卸载成本波动，其中每个智能体可隐式估计成本并自适应选择资源，以应对波动的供需动态。通过仿真评估表明：这种针对不确定性鲁棒性和动态适应性的个体扰动策略，可确保社会总体福祉达到一定最优水平——例如，在未知非对称属性下使实际博弈序列收敛，并降低因智能体自利行为所造成的社会福利成本。