Enhancing Conversational AI with Reinforcement Learning for Multi-turn Dialogue Management

Abstract

This paper investigates the application of reinforcement learning (RL) to bolster the performance of conversational AI in navigating multi-round dialogues. Traditional dialogue systems frequently rely on predetermined rules or supervised learning techniques, which can restrict their ability to adapt to evolving conversational contexts. By integrating RL, we aim to create versatile and responsive dialogue managers that optimize long-term user gratification and engagement.