The background problem
Many chatbots are created with regular machine learning natural language processing algorithms, and these focus on immediate responses. A new concept is to create chatbots with the use of deep reinforcement learning. This would mean that the future implications of our immediate responses would be considered to maintain coherence.
In this chapter, you will learn how to apply deep reinforcement learning to natural language processing. Our reward function will be a future-looking function, and you will learn how to think probabilistically through the creation of this function.
Dataset
The dataset that we will use mainly consists of conversations from selected movies. This dataset will help to stimulate and understand conversational methods in the chatbot. Also, there are movie lines, which are essentially the same as the movie conversations, albeit shorter exchanges between people. Other data sets that will be used include some containing movie titles, movie characters,...
