Continuous Learning Conversational AI: A Personalized Agent Framework via A2C Reinforcement Learning
This addresses the problem of static conversational AI for users seeking personalized interactions, though it appears incremental as it builds on existing reinforcement learning and LLM methods.
The paper tackled the challenge of creating personalized and adaptable conversational AI by introducing a Continuous Learning Conversational AI (CLCA) approach using A2C reinforcement learning, which learns to optimize conversation strategies for personalization in simulated sales dialogues.
Creating personalized and adaptable conversational AI remains a key challenge. This paper introduces a Continuous Learning Conversational AI (CLCA) approach, implemented using A2C reinforcement learning, to move beyond static Large Language Models (LLMs). We use simulated sales dialogues, generated by LLMs, to train an A2C agent. This agent learns to optimize conversation strategies for personalization, focusing on engagement and delivering value. Our system architecture integrates reinforcement learning with LLMs for both data creation and response selection. This method offers a practical way to build personalized AI companions that evolve through continuous learning, advancing beyond traditional static LLM techniques.