3D Design through Interactive Training of a Generative Reinforcement Learning Agent

Designing is an open-ended process that involves multiple iterations of creating artifacts and evaluating them with multifaceted criteria. The challenges come from the fact that not only the artifact, but the criteria for design are gradually articulated and that they are unique to the domain, context, and designer’s intentions. Recent advancements in generative design have sought to aid the 3D design process by delegating the task of auto generating and visualizing multiple artifacts. However, these frameworks still come short by enforcing the designers to prematurely define constraints and prompting them to translate their intentions into a limited set of parametric or operational inputs which is non-trivial and confines the design to certain forms. 

This thesis introduces an alternative framework of 3D designing where the designer interactively trains a reinforcement learning (RL) based generative agent. It first aims to formulate a RL agent that can be trained to procedurally generate 3D forms given partial and evolving constraints. The proposed method leverages the capabilities of RL agents to procedurally generate feasible 3D forms while adapting to user-provided constraints. It also presents and experiments with an interactive framework between the designer and generative RL agent, that would allow simultaneous articulation of design criteria and artifacts, which is critical in early design development. The interactive framework allows designers to iteratively evaluate auto-generated artifacts and provide intermittent feedback that serves to articulate the implicit design criteria. As a result, this work sheds light on ways to integrate learning-based computational frameworks for idiosyncratic design tasks as well as presenting a novel mode of designing through training generative learning-based agents.