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Reinforcement Learning (RL)-based algorithmic solutions have been profusely proposed in recent years for addressing multiple problems in the Radio Access Network (RAN). However, how RL algorithms have to be trained for a successful exploitation has n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7513592b93759fabc2da2d0368035c4b
https://hdl.handle.net/2117/380800
https://hdl.handle.net/2117/380800
