Continuous Learning Conversational AI: A Personalized Agent Framework via A2C Reinforcement Learning

• URL: http://arxiv.org/abs/2502.12876v1
• Date: Tue, 18 Feb 2025 14:05:59 GMT
• Title: Continuous Learning Conversational AI: A Personalized Agent Framework via A2C Reinforcement Learning
• Authors: Nandakishor M, Anjali M,
• Abstract summary: This paper introduces a Continuous Learning Conversational AI (CLCA) approach, implemented using A2C reinforcement learning.<n>We use simulated sales dialogues, generated by Large Language Models (LLMs), to train an A2C agent.<n>This agent learns to optimize conversation strategies for personalization, focusing on engagement and delivering value.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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