  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
