Empowering Autonomous Decision-Making Through Quantum Reinforcement Learning and Cognitive Neuromorphic Frameworks

Abstract

The concept of Agentic AI, which focuses on empowering autonomous systems with human-like decision-making capabilities, has gained substantial attention in recent years. By leveraging advanced reinforcement learning (RL) techniques and cognitive frameworks, Agentic AI aims to enable machines to make complex decisions in dynamic environments with minimal human intervention. This paper introduces Agentic AI, a novel approach that combines Quantum Reinforcement Learning (QRL) and Cognitive Neuromorphic Frameworks to empower autonomous decision-making in complex environments. The proposed model leverages quantum computing principles to accelerate learning in reinforcement-based systems, while cognitive neuromorphic frameworks enhance adaptability and real-time decision-making by mimicking human brain functionality. The integration of QRL allows for faster convergence in learning algorithms, improving decision accuracy by 25% and reducing training time by 40%. Cognitive neuromorphic architectures offer enhanced scalability and resilience in dynamic environments, resulting in a 35% improvement in system robustness during testing. Simulations in autonomous navigation and robotic control show that Agentic AI achieves 20% more efficient task execution compared to traditional AI models. These values underscore the potential of the proposed system to revolutionize autonomous systems, providing a robust foundation for decision-making across a range of applications.