Designing Agents for Co-Creation in Virtual Reality with Deep Reinforcement Learning

The ways that we embody interactions in virtual spaces are rapidly evolving with developments in the fields of deep learning and virtual reality. This is significant because these spaces are increasingly becoming extensions of different human social and creative practices. That being said, there is a lack of perspectives offered towards shaping technological usage that considers creative practices alongside immersive platforms and cutting-edge AI technologies. How then might we as designers, developers, and users, interrogate the intersection of deep learning and VR to better understand how the future landscape of design and virtual interaction might change? This thesis offers insight into how these technologies might overlap in future design environments. This thesis provides a framework for a design interaction between human and agent co-creator in VR supported by reinforcement learning, an unsupervised training method focused on crafting AI behavior. This thesis contributes in two ways, by firstly providing insight into how human-agent interactions can be facilitated given open-ended design problems, and secondly, by exploring RL as a design medium for crafting agent behaviors through creating human demonstrations in VR. This thesis helps create and guide future questioning on the relevancy of emerging AI technologies in open-ended design contexts.