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Industry has always been in the pursuit of becoming more economicallyefficient and the current focus has been to reduce human labour using moderntechnologies. Even with cutting edge technologies, which range from packagingrobots to AI for fault detection, there is still some ambiguity on the aims ofsome new systems, namely, whether they are automated or autonomous. In thispaper we indicate the distinctions between automated and autonomous system aswell as review the current literature and identify the core challenges forcreating learning mechanisms of autonomous agents. We discuss using differenttypes of extended realities, such as digital twins, to train reinforcementlearning agents to learn specific tasks through generalization. Oncegeneralization is achieved, we discuss how these can be used to developself-learning agents. We then introduce self-play scenarios and how they can beused to teach self-learning agents through a supportive environment whichfocuses on how the agents can adapt to different real-world environments.

Autonomous Industrial Management via Reinforcement Learning: Self-Learning Agents for Decision-Making -- A Review