In recent years there has been a sharp rise in applications, in which significant events need to be classified but only a few training instances are available. These are known as cases of one-shot learning. To handle this challenging task, organizations often use human analysts to classify events under high uncertainty. Existing algorithms use a threshold-based mechanism to decide whether to classify an object automatically or send it to an analyst for deeper inspection. However, this approach leads to a significant waste of resources since it does not take the practical temporal constraints of system resources into account. By contrast, the focus in this paper is on rigorously optimizing the resource consumption in the system which applies to broad application domains, and is of a significant interest for academic research, industrial developments, as well as society and citizens benefit. The contribution of this paper is threefold. First, a novel Deep Reinforcement One-shot Learning (DeROL) framework is developed to address this challenge. The basic idea of the DeROL algorithm is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data. Then, in real-time, DeROL maps the current state of the one-shot learning process to operational actions based on the trained deep-Q network, to maximize the objective function. Second, the first open-source software for practical artificially intelligent one-shot classification systems with limited resources is developed for the benefit of researchers and developers in related fields. Third, an extensive experimental study is presented using the OMNIGLOT dataset for computer vision tasks, the UNSW-NB15 dataset for intrusion detection tasks, and the Cleveland Heart Disease Dataset for medical monitoring tasks that demonstrates the versatility and efficiency of the DeROL framework.

近年来，应用程序急剧增加，其中需要对重大事件进行分类，但只有少数训练实例可用。这些被称为一次学习的情况。为了处理这一具有挑战性的任务，组织经常使用人工分析人员对高度不确定性下的事件进行分类。现有算法使用基于阈值的机制来决定是自动对对象进行分类还是将其发送给分析人员进行更深入的检查。但是，由于这种方法没有考虑到系统资源的实际时间限制，因此导致资源的大量浪费。相比之下，本文的重点是严格优化系统中的资源消耗，该系统适用于广泛的应用领域，并且对于学术研究具有重大意义，工业发展，以及社会和公民受益。本文的贡献是三方面的。首先，开发了一种新颖的深度强化单发学习（DeROL）框架来应对这一挑战。DeROL算法的基本思想是训练一个深度Q网络来获得一个忽略了测试数据中看不见的类。然后，DeROL实时基于训练的Deep-Q网络将单次学习过程的当前状态映射到操作动作，以最大化目标功能。其次，为相关领域的研究人员和开发人员开发了首个用于资源有限的实用人工智能单次分类系统的开源软件。第三，利用用于计算机视觉任务的OMNIGLOT数据集，用于入侵检测任务的UNSW-NB15数据集以及用于医疗监视任务的Cleveland心脏病数据集，进行了广泛的实验研究，证明了DeROL框架的多功能性和效率。