A DRL policy with graph attention learns selective merging for deadline-constrained coded caching, cutting packet expiration ratio by 40.9% versus SACM++ while merging only about 32% of the time.
A reinforcement-learning approach to proactive caching in wireless networks,
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Learning Selective Merge Policies for Deadline-Constrained Coded Caching via Deep Reinforcement Learning
A DRL policy with graph attention learns selective merging for deadline-constrained coded caching, cutting packet expiration ratio by 40.9% versus SACM++ while merging only about 32% of the time.