  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.
