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 systems 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, how to train reinforcement learning agents to learn specific tasks through generalisation. Once generalisation 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 that focuses on how the agents can adapt to different environments. We introduce an initial prototype of our ideas by solving a multi-armed bandit problem using two ε-greedy algorithms. Further, we discuss future applications in the industrial management realm and propose a modular architecture for improving the decision-making process via autonomous agents.

中文翻译：

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通过强化学习实现自主工业管理

工业界一直在寻求提高经济效率，当前的重点是使用现代技术减少人工。即使采用最先进的技术，从包装机器人到用于故障检测的AI，其某些新系统的目标仍然存在歧义，即它们是自动化还是自治。在本文中，我们指出了自动化系统与自治系统之间的区别，并回顾了当前文献并确定了创建自治代理学习机制的核心挑战。我们讨论使用不同类型的扩展现实，例如数字双胞胎，如何训练强化学习代理以通过概括学习特定任务。一旦实现了概括，我们将讨论如何将其用于开发自学代理。然后，我们介绍自我扮演的场景，以及如何通过支持性环境将其用于教自学代理，该支持性环境侧重于代理如何适应不同的环境。通过使用两个ε-贪心算​​法解决多臂匪问题，我们引入了思想的初始原型。此外，我们讨论了工业管理领域中的未来应用，并提出了一种模块化架构，用于通过自主代理改进决策过程。