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Multi-Agent Reinforcement Learning (MARL) is a challenging subarea ofReinforcement Learning due to the non-stationarity of the environments and thelarge dimensionality of the combined action space. Deep MARL algorithms havebeen applied to solve different task offloading problems. However, inreal-world applications, information required by the agents (i.e. rewards andstates) are subject to noise and alterations. The stability and the robustnessof deep MARL to practical challenges is still an open research problem. In thiswork, we apply state-of-the art MARL algorithms to solve task offloading withreward uncertainty. We show that perturbations in the reward signal can inducedecrease in the performance compared to learning with perfect rewards. Weexpect this paper to stimulate more research in studying and addressing thepractical challenges of deploying deep MARL solutions in wirelesscommunications systems.

Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty