Deep Reinforcement Learning in Immersive Virtual Reality Exergame for Agent Movement Guidance

摘要

Immersive Virtual Reality applied to exercise games has a unique potential to both guide and motivate users in performing physical exercise. Advances in modern machine learning open up new opportunities for more significant intelligence in such games. To this end, we investigate the following research question: What if we could train a virtual robot arm to guide us through physical exercises, compete with us, and test out various double-jointed movements? This paper presents a new game mechanic driven by artificial intelligence to visually assist users in their movements through the Unity Game Engine, Unity MI-Agents, and the HTC Vive Head-Mounted Display. We discuss how deep reinforcement learning through Proximal Policy Optimization and Generative Adversarial Imitation Learning can be applied to complete physical exercises from the same immersive virtual reality game. We examine our mechanics with four users through protecting a virtual butterfly with an agent that visually helps users as a cooperative “ghost arm” and an independent competitor. Our results suggest that deep learning agents are effective at learning game exercises and may provide unique insights for users.