Task-oriented Design through Deep Reinforcement Learning
This addresses the challenge of automating creative design processes for product designers, though it appears incremental as it applies existing RL methods to a new domain.
The paper tackles the problem of bridging the gap between problem and solution in design by applying deep reinforcement learning to optimize task-oriented 3D product design, achieving satisfactory results even for multi-goal tasks and validating its assistance to designers' creativity.
We propose a new low-cost machine-learning-based methodology which assists designers in reducing the gap between the problem and the solution in the design process. Our work applies reinforcement learning (RL) to find the optimal task-oriented design solution through the construction of the design action for each task. For this task-oriented design, the 3D design process in product design is assigned to an action space in Deep RL, and the desired 3D model is obtained by training each design action according to the task. By showing that this method achieves satisfactory design even when applied to a task pursuing multiple goals, we suggest the direction of how machine learning can contribute to the design process. Also, we have validated with product designers that this methodology can assist the creative part in the process of design.