Farnaz Niknia

Fast-Convergent Meta-RL via Gradient-Clustered BS Sampling for Edge Caching

A meta-reinforcement learning framework for caching across independent, non-overlapping BSs that directly targets this bottleneck, and introduces gradient-based clustering, which groups BSs by local gradient similarity and draws from every cluster, in proportion to its size, at each meta-iteration.