Fast-Convergent Meta-RL via Gradient-Clustered BS Sampling for Edge Caching
Wireless edge caching networks typically consist of many independent Base Stations (BSs), each facing its own request rate and content popularity profile. Training a Reinforcement Learning (RL) caching agent from scratch at every BS forces each agent to relearn, through slow trial and error, a decision problem that is structurally identical across the network. Meta-reinforcement learning removes this redundancy by learning a shared initialization that adapts to any BS in a few local updates; however, meta-training itself becomes the bottleneck at scale: the meta-gradient must be estimated from a small subset of BSs at each meta-iteration, and sampling this subset uniformly at random yield...
| Authors | Farnaz Niknia, Ping Wang |
|---|---|
| Primary category | cs.NI |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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