  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.
