DMADRL: A Distributed Multi-agent Deep Reinforcement Learning Algorithm for Cognitive Offloading in Dynamic MEC Networks - Yi, Meng; Yang, Peng; Du, Miao; Ma, Ruochen (Neural processing letters, 2022)
Task offloading for mobile devices (MDs) has been extensively studied based on the feature that multi-access edge computing (MEC) can efficiently provide computing and storage resources at the edge of the network. Most existing researches mainly focus on offloading the tasks to the MEC serves to reduce computing energy consumption, while the utilization of idle resources of MDs and the dynamic of users are often ignored. To address this issue, we propose a distributed multi-agent deep reinforcement learning algorithm (DMADRL) with a hybrid cognitive offloading strategy for dynamic MEC networks, in which MDs can use their idle resources to process tasks directly or act as relay nodes to forward tasks to the MEC servers. Taking channel gain and power allocation into account, DMADRL aims to solve an optimal task computation offloading policy in the dynamic MEC network where MDs are moving at a certain rate, and its objective is to maximize the system data transmission rate. Specifically, by modeling this optimal problem as a Markov decision process (MDP), DMADRL integrates two critical phases of centralized training and distributed implementation to overcome the problem that the traditional DRL algorithm cannot converge in multi-agent scenarios. Moreover, DMADRL also considers the experience-sharing mechanism and strategy integration optimization. Simulation results show that our algorithm has excellent convergence, and compared with the other three classical algorithms, our algorithm DMADRL performs better in the cognitive offloading decision.