In this chapter, we learned how reinforcement learning can disrupt the domain of NLP. We studied the reasons behind the use of reinforcement learning in NLP. We covered two big application domains in NLP, that is, text summarization and question answering, and understood the basics of how a reinforcement learning framework was implemented in the existing models to obtain state-of-the-art results. There are other application domains in NLP where reinforcement learning has been implemented, such as dialog generation and machine translation (discussing them is out of the scope of this book).
This brings us to the end of this amazing journey of deep reinforcement learning. We started with the basics by understanding the concepts, then implemented those concepts using TensorFlow and OpenAI Gym, and went through cool research areas where deep reinforcement learning is being...
