  Multi-Agent Reinforcement Learning (MARL) is a challenging subarea of
Reinforcement Learning due to the non-stationarity of the environments and the
large dimensionality of the combined action space. Deep MARL algorithms have
been applied to solve different task offloading problems. However, in
real-world applications, information required by the agents (i.e. rewards and
states) are subject to noise and alterations. The stability and the robustness
of deep MARL to practical challenges is still an open research problem. In this
work, we apply state-of-the art MARL algorithms to solve task offloading with
reward uncertainty. We show that perturbations in the reward signal can induce
decrease in the performance compared to learning with perfect rewards. We
expect this paper to stimulate more research in studying and addressing the
practical challenges of deploying deep MARL solutions in wireless
communications systems.
