Deep Reinforcement Learning for Smart Steel Processing

顺序决策描述了决策者在做出最终决策之前对过程进行连续观察的情况。随着人工智能的发展，特别是“深度学习”（人工神经网络），在开发能够自己做出顺序决策的计算机代理方面取得了很大进展。在这个博士项目中，我们将开发先进的人工智能算法，用于智能钢铁加工中的顺序决策。该博士项目是EPSRC资助的SUSTAIN项目的一部分，该项目由英国5家主要钢铁生产商共同创建（塔塔、利伯蒂、英国钢铁、塞尔萨、谢菲尔德锻造师）和三所在这一领域具有专长的主要大学（斯旺西，沃里克和谢菲尔德）提供钢铁创新领域的学术领导。尽管钢铁行业产生“大数据”已经超过30年，但迄今为止生产效益有限。在这个博士项目中，我们将开发新的数据驱动技术，利用数据科学和机器学习的最新进展。最终，我们将为智能钢铁加工提供一个人工智能系统，能够自动化和优化某些仍然严重依赖人工干预的流程。我们将利用现有的历史数据库，这些数据库是通过我们的工业合作和下一代传感器的可用性提供的，这些传感器现在正在极端环境中取代传统的采样方法。我们还将开发一个“数字孪生”，一个基于模拟的环境，以帮助我们测试和开发新的强化学习算法。

Sequential decision making describes a situation where the decision maker makes successive observations of a process before a final decision is made. With recent advanced in artificial intelligence, and especially "deep learning" (artificial neural networks), much progress has been made in developing computer agents that are able to make sequential decision on their own. In this PhD project we will develop advanced artificial intelligence algorithms for sequential decision making for applications in smart steel processing.The PhD project is part of SUSTAIN, an EPSRC-funded programme co-created by the 5 major UK steel producers (Tata, Liberty, British Steel, Celsa, Sheffield Forgemasters) and the three principal Universities that have expertise in this area (Swansea, Warwick and Sheffield) to provide academic leadership in the field of steel innovation. Although the steel industry has generated "big data" for over 30 years, the production benefits have been limited so far. In this PhD project, we will develop novel data-driven techniques that leverage the latest advances in data science and machine learning. Ultimately, we will deliver an AI system for Smart Steel Processing able to automate and optimise certain processes that still rely heavily on manual intervention. We will exploit existing historical data repositories made available by our industrial collaboration and the availability of next-generation sensors that are now replacing traditional sampling methods in extreme environments. We will also develop a "digital twin", a simulation-based environment to help us test and develop novel reinforcement learning algorithms.