论文标题
设计过程是增强学习问题
Design Process is a Reinforcement Learning Problem
论文作者
论文摘要
尽管在过去的几年中，在研究中广泛使用了增强学习，但由于RL算法所遭受的一些弱点，例如在从模拟器过渡到现实世界中的某些弱点，它发现现实世界中的应用少于监督学习。在这里，我们认为设计过程是一个加强学习问题，并且可能是RL算法的适当应用，因为它是一个离线过程，并且通常在CAD软件中完成 - 一种模拟器。这为使用RL方法创造了机会，同时又带来了挑战。尽管设计过程是如此多样化，但在这里我们关注太空布局计划（SLP），将其作为马尔可夫决策过程中的RL问题，并使用PPO来解决布局设计问题。为此，我们开发了一个名为rldesigner的环境，以模拟SLP。 RLDESIGNER是一个兼容的OpenAi Gym兼容环境，可以轻松自定义以定义各种设计方案。我们公开共享环境，以鼓励RL和建筑社区使用它来测试不同的RL算法或在其设计实践中。这些代码可在以下github存储库中可用https://github.com/ rezakakooee/rldesigner/tree/second_paper
While reinforcement learning has been used widely in research during the past few years, it found fewer real-world applications than supervised learning due to some weaknesses that the RL algorithms suffer from, such as performance degradation in transitioning from the simulator to the real world. Here, we argue the design process is a reinforcement learning problem and can potentially be a proper application for RL algorithms as it is an offline process and conventionally is done in CAD software - a sort of simulator. This creates opportunities for using RL methods and, at the same time, raises challenges. While the design processes are so diverse, here we focus on the space layout planning (SLP), frame it as an RL problem under the Markov Decision Process, and use PPO to address the layout design problem. To do so, we developed an environment named RLDesigner, to simulate the SLP. The RLDesigner is an OpenAI Gym compatible environment that can be easily customized to define a diverse range of design scenarios. We publicly share the environment to encourage both RL and architecture communities to use it for testing different RL algorithms or in their design practice. The codes are available in the following GitHub repository https://github.com/ RezaKakooee/rldesigner/tree/Second_Paper
