DRL-Based Cross-Regional Computation Offloading Algorithm

In the field of edge computing, achieving low-latency computational task offloading with limited resources is a critical research challenge, particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications. In scenarios where edge servers are sparsely deployed, the lack of coordination and information sharing often leads to load imbalance, thereby increasing system latency. Furthermore, in regions without edge server coverage, tasks must be processed locally, which further exacerbates latency issues. To address these challenges, we propose a novel and efficient Deep Reinforcement Learning (DRL)-based approach aimed at minimizing average task latency. The proposed method incorporates three offloading strategies: local computation, direct offloading to the edge server in local region, and device-to-device (D2D)-assisted offloading to edge servers in other regions. We formulate the task offloading process as a complex latency minimization optimization problem. To solve it, we propose an advanced algorithm based on the Dueling Double Deep Q-Network (D3QN) architecture and incorporating the Prioritized Experience Replay (PER) mechanism. Experimental results demonstrate that, compared with existing offloading algorithms, the proposed method significantly reduces average task latency, enhances user experience, and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.