• Kodamasimham Krishna

The rapid advancements in artificial intelligence (AI) have led to the development of increasingly autonomous systems capable of performing complex tasks with minimal human intervention. However, the journey toward fully autonomous AI systems is hindered by challenges related to adaptability, scalability, and transparency. This paper explores the integration of three key AI methodologies—Reinforcement Learning (RL), Generative Models, and Explainable AI (XAI)—to address these challenges and pave the way for next-generation autonomous systems. RL provides a robust framework for learning optimal behaviors through interaction with dynamic environments, while generative models enhance these capabilities by simulating diverse scenarios and augmenting training data. XAI ensures that the decision-making processes of these AI systems are transparent and interpretable, fostering trust and accountability. By unifying these approaches, we propose a comprehensive framework that leverages their synergies to create more robust, adaptable, and trustworthy AI systems. Through detailed exploration of each methodology, practical case studies, and proposed integration strategies, this paper aims to contribute to the advancement of autonomous AI and its applications in fields such as robotics, autonomous vehicles, and healthcare.

Reinforcement Learning, Generative Models, Explainable AI, Autonomous Systems, Artificial Intelligence, Machine Learning.

"Towards Autonomous AI: Unifying Reinforcement Learning, Generative Models, and Explainable AI for Next-Generation Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.60-68, April-2020, Available :http://www.jetir.org/papers/JETIR2004643.pdf

"Towards Autonomous AI: Unifying Reinforcement Learning, Generative Models, and Explainable AI for Next-Generation Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp60-68, April-2020, Available at : http://www.jetir.org/papers/JETIR2004643.pdf