  Nowadays, the current neural network models of dialogue generation(chatbots)
show great promise for generating answers for chatty agents. But they are
short-sighted in that they predict utterances one at a time while disregarding
their impact on future outcomes. Modelling a dialogue's future direction is
critical for generating coherent, interesting dialogues, a need that has led
traditional NLP dialogue models that rely on reinforcement learning. In this
article, we explain how to combine these objectives by using deep reinforcement
learning to predict future rewards in chatbot dialogue. The model simulates
conversations between two virtual agents, with policy gradient methods used to
reward sequences that exhibit three useful conversational characteristics: the
flow of informality, coherence, and simplicity of response (related to
forward-looking function). We assess our model based on its diversity, length,
and complexity with regard to humans. In dialogue simulation, evaluations
demonstrated that the proposed model generates more interactive responses and
encourages a more sustained successful conversation. This work commemorates a
preliminary step toward developing a neural conversational model based on the
long-term success of dialogues.

Computation and Language;Artificial Intelligence;Machine Learning