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In this paper, a novel opportunistic scheduling (OS) scheme with antenna selection (AS) for the energy harvesting (EH) cooperative communi-cation system where the relay can harvest energy from the source transmission is proposed. In this consid-ered scheme, we take into both traditional mathemati-cal analysis and reinforcement learning (RL) scenarios with the power splitting (PS) factor constraint. For the case of traditional mathematical analysis of a fixed-PS factor, we derive an exact closed-form expressions for the ergodic capacity and outage probability in general signal-to-noise ratio (SNR) regime. Then, we com-bine the optimal PS factor with performance metrics to achieve the optimal transmission performance. Subse-quently, based on the optimized PS factor, a RL tech-nique called as Q-learning (QL) algorithm is proposed to derive the optimal antenna selection strategy. To highlight the performance advantage of the proposed QL with training the received SNR at the destina-tion, we also examine the scenario of QL scheme with training channel between the relay and the destination. The results illustrate that, the optimized scheme is al-ways superior to the fixed-PS factor scheme. In addi-tion, a better system parameter setting with QL signif-icantly outperforms the traditional mathematical anal-ysis scheme.