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Multicasting is an efficient technique for simultaneously transmitting commonmessages from the base station (BS) to multiple mobile users (MUs). Multicastscheduling over multiple channels, which aims to jointly minimize the energyconsumption of the BS and the latency of serving asynchronized requests fromthe MUs, is formulated as an infinite-horizon Markov decision process (MDP)problem with a large discrete action space, multiple time-varying constraints,and multiple time-invariant constraints. To address these challenges, thispaper proposes a novel distribution-embedding multi-agent proximal policyoptimization (DE-MAPPO) algorithm, which consists of one modified MAPPO and onedistribution-embedding module: The former one handles the large discrete actionspace and time-varying constraints by modifying the structure of the actornetworks and the training kernel of the conventional MAPPO; and the latter oneiteratively adjusts the action distribution to satisfy the time-invariantconstraints. Moreover, a performance upper bound of the considered MDP isderived by solving a two-step optimization problem. Finally, numerical resultsdemonstrate that our proposed algorithm outperforms the existing ones andachieves comparable performance to the derived benchmark.

Multicast Scheduling over Multiple Channels: A Distribution-Embedding Deep Reinforcement Learning Method