Reinforcement Learning for Self-Improving AI Chatbots in Personalized Assistants

In this paper, propose a reinforcement learning (RL) based approach to construct self-improvable AI chat-bots as personalize virtual assistant. Going beyond traditional rulebased systems, our model leverages user interactions to generate responses and tailor to specific preferences. The feedback-based rewards, which include task success, user satisfaction, and conversation quality, allow the chatbot to improve its dialogue strategy with time. These advanced RL methods make use of gathering all the possible and probable actions that the chatbot can take in response to a multi-turn conversation and helps it choose the best of them to use. The system incorporates user context, conversation history, and behavior patterns to enhance personalization and make the assistant more relatable and efficient. Humans in the loop training and feedback loops prevent wrong or unnecessary answered information through the system. Experimentation shows a substantial improvement in conversation quality, task completion and end-user engagement. This work lays the groundwork for AI chatbots that will become smarter, more useful and more human-like the more they interact.