ECMSA: Dual-Agent Learning-Based Edge Caching with Multi-Strategy Adaptation in Dynamic Environments

With the proliferation of mobile devices and IoT applications, edge caching has become vital for mitigating network congestion and enhancing user Quality of Experience (QoE). However, traditional caching policies, such as Least Frequently Used (LFU), First-In-First-Out (FIFO), and Least Recently Used (LRU), often struggle to perform effectively in highly dynamic and heterogeneous environments, particularly when content sizes vary significantly. Moreover, existing approaches, whether AI-driven or heuristic-based, typically adopt a single caching strategy, which inherently limits their flexibility and adaptability. To address these limitations, we propose ECMSA, a learning-based multi-strategy edge caching algorithm that integrates a reinforcement learning-driven proactive caching strategy with three conventional reactive caching strategies. Specifically, ECMSA operates in two stages: First, it generates four candidate cache lists - three derived from traditional caching policies (LFU, FIFO, LRU) and one produced by our self-attention-enhanced Deep Deterministic Policy Gradient (Atten-Actor DDPG)-based proactive caching strategy. Next, it employs another Atten-Actor DDPG agent to dynamically select the optimal strategy in real time, leveraging current state features. This dual-agent framework enables continuous learning and adaptation of caching decisions, effectively optimizing content placement and update policies in response to evolving user demands. Extensive experiments conducted on both synthetic and real-world datasets demonstrate that ECMSA achieves 15-17% higher cache-hit ratios and 16-22% lower latency than baseline methods under constrained cache capacities and diverse content sizes. Furthermore, ECMSA exhibits strong robustness and generalization ability, allowing it to rapidly adapt to unseen environments.