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A Meta-Reinforcement Learning Approach to Process Control

机译：过程控制的元增强学习方法

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摘要

Meta-learning is a branch of machine learning which aims to quickly adapt models, such as neural networks, to perform new tasks by learning an underlying structure across related tasks. In essence, models are being trained to learn new tasks effectively rather than master a single task. Meta-learning is appealing for process control applications because the perturbations to a process required to train an AI controller can be costly and unsafe. Additionally, the dynamics and control objectives are similar across many different processes, so it is feasible to create a generalizable controller through meta-learning capable of quickly adapting to different systems. In this work, we construct a deep reinforcement learning (DRL) based controller and meta-train the controller using a latent context variable through a separate embedding neural network. We test our meta-algorithm on its ability to adapt to new process dynamics as well as different control objectives on the same process. In both cases, our meta-learning algorithm adapts very quickly to new tasks, outperforming a regular DRL controller trained from scratch. Meta-learning appears to be a promising approach for constructing more intelligent and sample-efficient controllers.
机译：元学习是机器学习的分支，旨在快速适应神经网络，例如神经网络，通过学习相关任务的底层结构来执行新任务。从本质上讲，培训模型可以有效地学习新任务，而不是掌握单一任务。元学习是对过程控制应用的吸引力，因为培训AI控制器所需的过程的扰动可能是昂贵的并且不安全。此外，在许多不同的过程中，动态和控制目标在许多不同的过程中类似，因此通过能够快速适应不同系统的元学习创建一个更广泛的控制器是可行的。在这项工作中，我们通过单独的嵌入神经网络使用潜在的上下文变量来构建基于深度加强学习（DRL）的控制器和Meta-Train。我们在其适应新流程动态的能力和同一过程中的不同控制目标上测试了我们的元算法。在这两种情况下，我们的元学习算法非常快速地适应新任务，优于从头开始培训的常规DRL控制器。元学习似乎是构建更智能和采样高效控制器的有希望的方法。

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