Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing

As mobile applications grow in complexity, there is an increasing need to perform computationally intensive tasks. However, user devices (UDs), such as tablets and smartphones, have limited capacity to carry out the required computations. Task offloading in mobile edge computing (MEC) is a strategy that meets this demand by distributing tasks between UDs and servers. Deep reinforcement learning (DRL) is a promising solution for this strategy because it can adapt to dynamic changes and minimize online computational complexity. However, various types of continuous and discrete resource constraints on UDs and MEC servers pose challenges to the design of an efficient DRL algorithm. Existing DRL-based task-offloading algorithms focus on the constraints of the UDs, assuming the availability of enough resources on the server. Moreover, existing Multiagent DRL (MADRL)-based task-offloading algorithms are homogeneous agents and consider homogeneous constraints as a penalty in their reward function. We propose a novel Client-Master MADRL (CMMADRL) algorithm for task offloading in MEC that uses client agents at the UDs to decide on their resource requirements and a master agent at the server to make a combinatorial action selection based on the decision of the UDs. CMMADRL is shown to achieve up to 59% improvement in performance over existing benchmark and heuristic algorithms.