With the growing demand for a high-quality life, visual comfort (VC) is becoming increasingly important for improving the quality of underground spaces. The underground space landscape features can be defined by the spatial and material parameters of the components. This study proposes a novel parametric generative network (StepGN) for the 3D generative design of VC. It combines parametric modeling, VC evaluation, and a novel reinforcement learning (RL) model called encoded soft actor critic (ESAC) and simplifies the optimization of complex VC scenes into a parameter-generating optimization process. Among them, parametric modeling is used to generate a 3D underground space scene with components and material properties through parameterization, and the optimization of parameters depends on the evaluation and RL model. The evaluation model provides reward value, and a new ESAC algorithm is developed. It combines the soft actor-critic (SAC) algorithm with the encoder process and by setting the reward with a confidence threshold. In addition, the Swin cosine distance (SCD) is used to measure the diversity of the generated scenes. A comparison of the policy types and range conversion methods proves that the stochastic policy and Sigmoid function are more suitable for the generative design of VC. By comparing StepGN with a generative adversarial network-based generative network (VCGN) and other RL processes, it shows that StepGN can generate discrete distributions of the VC levels and can realize a high-comfort level scene, and the training speed and stability are considerably improved. Finally, StepGN is applied for the optimization of the Wujiaochang subway station scene in Shanghai, and it is proved that the VC of the generated results can provide a high comfort level.

中文翻译：

---

基于参数化网络的地下空间视觉舒适度生成设计框架

随着人们对高品质生活需求的不断增长，视觉舒适度（VC）对于提升地下空间品质越来越重要。地下空间景观特征可以通过构件的空间参数和材料参数来定义。本研究提出了一种用于 VC 的 3D 生成设计的新型参数生成网络 (StepGN)。它结合了参数化建模、VC 评估和一种称为编码软演员评论家 (ESAC) 的新型强化学习 (RL) 模型，并将复杂 VC 场景的优化简化为参数生成优化过程。其中，参数化建模用于通过参数化生成具有组件和材料属性的3D地下空间场景，参数的优化取决于评估和RL模型。评估模型提供奖励值，并开发了新的ESAC算法。它将软演员评论家 (SAC) 算法与编码器过程相结合，并通过设置具有置信度阈值的奖励。此外，Swin余弦距离（SCD）用于衡量生成场景的多样性。策略类型和范围转换方法的比较证明，随机策略和Sigmoid函数更适合VC的生成设计。通过将 StepGN 与基于生成对抗网络的生成网络 (VCGN) 和其他 RL 过程进行比较，表明 StepGN 可以生成 VC 级别的离散分布，并且可以实现高舒适级别的场景，并且训练速度和稳定性相当可观改善。最后，