MACU: a multi-agent cache updating framework for IIoT networks - Maiti, Ritabrata (2025)
An important role played by industrial Internet of Things (IIoT) networks is supporting the operations of autonomous mobile robots (AMR) by leveraging multiple-access edge caching servers. At the same time, judicious content caching strategies are essential for minimizing content retrieval delays and the costs associated with updating caches. In this study, a novel multi-agent reinforcement learning (MARL)-based cache update strategy termed multi-agent cache update (MACU) is proposed. MACU leverages the multi-agent deep deterministic policy gradient (MADDPG) framework and aims to optimize cache updates to reduce AoI, minimize events where AoI exceeds acceptable levels, and ensure that the costs associated with performing cache updates are kept low. Furthermore, to mitigate the computational complexity of training agents with MACU, the MACU with Global Critic (MACU-GC) variant is introduced, which diverges from traditional MADDPG by employing a singular global critic for enhanced training efficiency. Extensive numerical evaluations showcase the proposed strategies’ superiority over conventional DRL-based caching methods, achieving significant improvements in AoI costs, caching costs, and AoI violationcosts, while effectively reducing content retrieval latency, enhancing hit rates, and optimizing AoI and link load metrics.