In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.

翻译：近年来，强化学习与多臂赌博机技术已在医疗、金融、推荐系统、机器人以及语音与自然语言处理等广泛现实应用领域引发变革。尽管多数针对语音及语言应用的强化学习算法主要借助其灵活的优化特性来改进深度神经网络的训练过程，但强化学习在奖励驱动适应性、状态表征、时序结构及泛化能力等方面的优势仍有待深入挖掘。本综述概述了强化学习与多臂赌博机的最新进展，并探讨了如何利用这些方法有效构建具备自适应性、交互性与可扩展性的模型，以解决语音与自然语言处理中的各类问题。