Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty
This addresses practical deployment challenges for deep MARL in wireless communications, but it is incremental as it applies existing methods to a new problem without novel solutions.
The paper tackles the problem of task offloading in multi-agent reinforcement learning under reward uncertainty, showing that perturbations in the reward signal lead to performance decreases compared to using perfect rewards.
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.