GR-MADRL: a multi-agent deep reinforcement learning framework with hamiltonian optimization for task offloading in vehicular fog computing

Abstract

Efficient task offloading strategies face considerable implementation barriers in vehicular fog computing (VFC) network contexts, due to dynamic vehicular mobility, fluctuating network conditions, and varying fog resource distribution. These complexities hinder efficient task offloading and resource utilization, leading to suboptimal quality of service (QoS) in terms of latency and energy efficiency. Existing deep reinforcement learning and optimization techniques struggle to adapt to these dynamic conditions, necessitating a more robust approach. This paper proposes an advanced task offloading framework that integrates gated recurrent unit-based multi-agent deep reinforcement learning (GR-MADRL) and dynamic Hamiltonian optimization (DHO). Our framework employs an attention-enhanced GRU network to process complex temporal network states, enabling effective feature prioritization and state prediction. Vehicles and fog nodes collaborate as autonomous agents through a multi-agent reinforcement learning system, supported by a central coordinator that constructs a global network view and computes optimal policies for local implementation. Additionally, we formulate the task offloading problem as a dynamic Hamiltonian optimization to maximize long-term rewards and ensure system stability. Extensive simulations demonstrate that GR-MADRL integrated with DHO significantly reduces task latency by 28.3%, lowers energy consumption by 35.1%, and improves task offloading success rates to 94.2%, outperforming baseline methods. These results highlight the potential of this approach to improve scalability, efficiency, and real-time decision making in VFC networks.