电力线与无线双模通信混合组网可实现优势互补，在电力物联网、智能家居等领域具备重要的应用前景。为提升双模通信MAC算法的接入灵活性与网络资源利用率，提出了一种基于深度强化学习的双模网关超帧时隙资源分配算法。首先构建了双模通信网关与终端的交互模型，梳理了超帧结构及每个阶段的具体执行步骤；接着定义了机器学习应用的关键奖励函数、状态空间与动作空间，由网关对超帧接入的吞吐量等参数进行监测统计，通过迭代学习与训练得到优化后的超帧时隙数、占比等参数。最后通过仿真验证了该算法的有效性与可靠性，将所提算法与固定超帧结构、无超帧结构等算法进行对比，分析了不同算法下网络吞吐量、平均时延、丢包率的表现以及关键参数对系统性能的影响规律。仿真结果表明，所提出的算法可有效提升系统吞吐量、时延等方面的性能，能够实现资源的灵活高效分配。

The hybrid networking of power line and wireless dual-mode communication can achieve complementary advantages, demonstrating significant application prospects in fields such as the Internet of Things for power systems and smart homes. To enhance the access flexibility and network resource utilization of dual-mode communication MAC algorithms, a superframe time slot resource allocation algorithm for dual-mode gateways based on deep reinforcement learning is proposed. Firstly, an interaction model between the dual-mode communication gateway and terminals is established, detailing the superframe structure and the specific execution steps of each phase. Secondly, key reward functions, state spaces, and action spaces for machine learning applications are defined, with the gateway monitoring and collecting parameters such as superframe access throughput. Through iterative learning and training, optimized parameters including the number and proportion of superframe time slots are obtained. Finally, simulations are conducted to verify the effectiveness and reliability of the proposed algorithm. Comparisons are made with fixed superframe structure and non-superframe structure algorithms, analyzing network throughput, average delay, packet loss rate under different algorithms, as well as the influence patterns of key parameters on system performance. Simulation results indicate that the proposed algorithm can effectively improve system performance in terms of throughput and delay, enabling flexible and efficient resource allocation.