  Industry has always been in the pursuit of becoming more economically
efficient and the current focus has been to reduce human labour using modern
technologies. Even with cutting edge technologies, which range from packaging
robots to AI for fault detection, there is still some ambiguity on the aims of
some new systems, namely, whether they are automated or autonomous. In this
paper we indicate the distinctions between automated and autonomous system as
well as review the current literature and identify the core challenges for
creating learning mechanisms of autonomous agents. We discuss using different
types of extended realities, such as digital twins, to train reinforcement
learning agents to learn specific tasks through generalization. Once
generalization is achieved, we discuss how these can be used to develop
self-learning agents. We then introduce self-play scenarios and how they can be
used to teach self-learning agents through a supportive environment which
focuses on how the agents can adapt to different real-world environments.
