Fast-Convergent Meta-RL via Gradient-Clustered BS Sampling for Edge Caching
Read the original on arXiv Machine Learning →The paper introduces a meta‑reinforcement learning framework for wireless edge caching that uses a shared initialization to adapt quickly to each base station (BS). Each BS runs a local PPO agent modeled as a Semi‑Markov Decision Process, while a shared meta‑policy is trained via a MAML‑style loop. To reduce the high variance of the meta‑gradient estimator caused by uniform random sampling of BSs, the authors propose gradient‑based clustering, grouping BSs by local gradient similarity and sampling proportionally from each cluster, proving that this yields a lower‑variance estimator under BS heterogeneity.
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