The authors propose a two-stage action-space compression for RL-based RIS resource allocation, claiming lossless Pareto mapping plus autoencoder compression yields faster training and lower cost, but the lossless claim is flawed.
Reconfigurable intelligent surfaces for 6G: Emerging hardware architectures, applications, and open challenges,
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Pareto-Aware Hierarchical Reinforcement Learning for Online Resource Allocation in RIS-assisted Large-Scale IoT Systems
The authors propose a two-stage action-space compression for RL-based RIS resource allocation, claiming lossless Pareto mapping plus autoencoder compression yields faster training and lower cost, but the lossless claim is flawed.