This paper describes the generation of sketches of small single-family dwellings that satisfy habitability requirements and are energy efficient. The proposed approach considers three stages in the generation process, and each one is based on a combination of shape grammars and reinforcement learning. First a set of very simple shape grammar rules is defined that are capable of generating a great variety of sketches. In order to guarantee the generation of sketches that are both suitable for habitation and energy efficient, a reinforcement learning process is applied on this set. Then the grammar so trained is used to generate only “good” sketches. More precisely, the learning process applies positive rewards to sketches that satisfy desired habitability and energy efficiency guidelines. As a result, sequences of grammar rules that lead to good sketches are identified.

In this paper we present the general approach followed to develop the system and describe in detail the procedure applied in the reinforcement learning process. Experimental results are also presented, to show convergence of the learning process, and to compare the obtained results with those of real designs. A standard energy simulation program is used to validate the approach.

本文描述了满足居住要求和节能的小型单户住宅草图的生成。所提出的方法考虑了生成过程中的三个阶段，每个阶段都基于形状语法和强化学习的组合。首先，定义了一组非常简单的形状语法规则，这些规则能够生成各种草图。为了保证生成既适合居住又高效节能的草图，在此场景上应用了强化学习过程。然后，经过如此训练的语法仅用于生成“良好”的草图。更准确地说，学习过程会向满足所需适居性和能效准则的草图施加积极奖励。结果是，

在本文中，我们介绍了开发系统所遵循的一般方法，并详细描述了在强化学习过程中应用的过程。还给出了实验结果，以表明学习过程的收敛性，并将获得的结果与实际设计进行比较。使用标准的能量模拟程序来验证该方法。