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Advancing RAN Slicing with Offline Reinforcement Learning
Dynamic radio resource management (RRM) in wireless networks presents significant challenges, particularly in the context of Radio Access Network (RAN) slicing.
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets
We observe that the performance of offline RL for the RRM problem depends critically on the behavior policy used for data collection, and further propose a novel offline RL solution that leverages heterogeneous datasets collected by different behavior policies.
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