Miss-Triggered Content Cache Replacement Under Partial Observability: Transformer-Decoder Q-Learning

Content delivery networks (CDNs) face steadily rising, uneven demand, straining heuristic cache replacement. Reinforcement learning (RL) is promising, but most work assumes a fully observable Markov Decision Process (MDP), unrealistic under delayed, partial, and noisy signals. We model cache replacement as a Partially Observable MDP (POMDP) and present the Miss-Triggered Cache Transformer (MTCT), a Transformer-decoder Q-learning agent that encodes recent histories with self-attention. MTCT invokes its policy only on cache misses to align compute with informative events and uses a delayed-hit reward to propagate information from hits. A compact, rank-based action set (12 actions by default) captures popularity–recency trade-offs with complexity independent of cache capacity. We evaluate MTCT on a real trace (MovieLens) and two synthetic workloads (Mandelbrot–Zipf, Pareto) against Adaptive Replacement Cache (ARC), Windowed TinyLFU (W-TinyLFU), classical heuristics, and Double Deep Q-Network (DDQN). MTCT achieves the best or statistically comparable cache-hit rates on most cache sizes; e.g., on MovieLens at M=600, it reaches 0.4703 (DDQN 0.4436, ARC 0.4513). Miss-triggered inference also lowers mean wall-clock time per episode; Transformer inference is well suited to modern hardware acceleration. Ablations support CL=50 and show that finer action grids improve stability and final accuracy.