中央空调冷源设备台数与运行参数的优化是一类离散与连续变量的协同优化问题，而经典强化学习算法难以优化此类问题。为此，该文提出了一种结合选项-评论者与演员-评论者框架的中央空调冷源系统节能优化控制策略。首先，采用分层演员-评论者（H-AC）算法分层优化设备台数与运行参数，且高层和底层模型共用Q网络评估状态价值，以解决多时间尺度下的优化难题；然后，在智能体架构、策略与网络更新方式等方面对H-AC算法进行改进，以加速智能体的收敛；最后，以夏热冬暖地区某科研办公建筑中央空调冷源系统为研究对象，基于冷源系统TRNSYS仿真平台进行实验。结果表明：在平均室内舒适时间占比分别增加14.08、11.23、29.70、9.07个百分比的前提下，基于改进H-AC算法的系统能耗分别比其他4种常规深度强化学习算法减少了32.28%、28.55%、28.63%、11.53%；虽然基于改进H-AC算法的系统能耗比基于选项-评论者框架的算法增加了0.27%，但获得了更平稳的学习过程且平均室内舒适时间占比增加了4.8个百分点。该文算法可为各类建筑中央空调冷源系统节能优化提供有效的技术手段，助力建筑“双碳”目标的实现。

The optimization of the number of central air-conditioning cooling source units and their operating parameters is a collaborative optimization problem involving both discrete and continuous variables, which poses challenges for classical reinforcement learning algorithms. To address this problem, this paper proposed an energy-saving optimization control strategy for central air-conditioning cooling source systems based on a combination of the options-critic and actor-critic frameworks. Firstly, a hierarchical actor-critic (H-AC) algorithm was utilized to hierarchically optimize the number of units and operating parameters, with both the high-level and low-level models sharing a Q-network to evaluate state values, thereby addressing optimization challenges across multiple time scales. Secondly, the H-AC algorithm was improved in terms of agent architecture, policy, and network update mechanisms to accelerate the convergence of the agent. Finally, the proposed method was validated on the cooling source system of a research building located in a hot summer and warm winter region, using a TRNSYS simulation platform for experiments. The results demonstrate that, under conditions where the average indoor comfort time proportion is increased by 14.08, 11.23, 29.70 and 9.07 percentage points, respectively, the system energy consumption based on the improved H-AC algorithm is reduced by 32.28%, 28.55%, 28.64%, and 11.53% compared to four classical DRL algorithms. Although the system energy consumption of the improved H-AC algorithm is 0.27% higher than that of the options-critic framework, it achieves a more stable learning process and increases the average indoor comfort time proportion by 4.8%. This approach offers effective technical solutions for energy-saving optimization of central air-conditioning cold source systems in various building types, contributing to the achievement of buildings’ dual-carbon goals.