Reinforcement learning for extended reality: Designing self-play scenarios

Abstract

A common problem for deep reinforcement learning networks is a lack of training data to learn specific tasks through generalization. In this paper, we discuss using extended reality to train reinforcement learning agents to overcome this problem. We review popular reinforcement learning and extended reality techniques and then synthesize the information, this allowed us to develop our proposed design for a self learning agent. Meta learning offers an important way forward, but the agents ability to perform self-play is considered crucial for achieving successful AI. Therefore, we focus on improving self-play scenarios for teaching self-learning agents, by providing a supportive environment for improved agent-environment interaction.