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In this letter, we investigate a joint power and beamforming design problemfor rate-splitting multiple access (RSMA)-based aerial communications withenergy harvesting, where a self-sustainable aerial base station serves multipleusers by utilizing the harvested energy. Considering maximizing the sum-ratefrom the long-term perspective, we utilize a deep reinforcement learning (DRL)approach, namely the soft actor-critic algorithm, to restrict the maximumtransmission power at each time based on the stochastic property of the channelenvironment, harvested energy, and battery power information. Moreover, fordesigning precoders and power allocation among all the private/common streamsof the RSMA, we employ sequential least squares programming (SLSQP) using theHan-Powell quasi-Newton method to maximize the sum-rate for the giventransmission power via DRL. Numerical results show the superiority of theproposed scheme over several baseline methods in terms of the average sum-rateperformance.

Sum-Rate Maximization of RSMA-based Aerial Communications with Energy Harvesting: A Reinforcement Learning Approach