DMT-PPO: Weight-Adaptive Multiobjective Task Offloading With Dynamic Preference Learning in Heterogeneous Edge Computing

Abstract

Multiaccess edge computing (MEC) has emerged as a key paradigm to empower resource-constrained edge devices through task offloading. However, dynamically fluctuating user preferences regarding latency and energy tradeoffs severely complicate the task scheduling and resource allocation, while existing methods struggle to accommodate this variability. To address this, we first formulate the dynamic-weight task offloading challenge as a nonconvex, nondeterministic polynomial-time (NP) hard multiobjective optimization problem (MOOP). We then propose a dynamic multiobjective task offloading framework built upon an enhanced proximal policy optimization algorithm to tackle this MOOP, namely DMT-PPO, which incorporates a dynamic reward structure and preference-aware subnetworks to effectively model user preferences and adapt to varying weight conditions. By introducing a discrete-continuous hybrid action space, DMT-PPO achieves the joint optimization across server selection, offloading decision, and computing provisioning. Moreover, DMT-PPO leverages a domain-based transfer learning (TL) paradigm coupled with an edge caching mechanism to accelerate both training and decision processes, enabling fine-grained coordination among heterogeneous task, device, and edge server settings. Extensive experiments demonstrate our framework’s superiority in dynamic environments, achieving up to 12.5% energy savings and a 10.6% reduction in task completion time, alongside a $3.5\times $ improvement in training efficiency compared to state-of-the-art baselines like Hybrid-PPO.