A Novel Self-Learning AI Framework for Adaptive Decision-Making in Dynamic Environments
Abstract
AI models often struggle to adapt to rapidly changing environments without extensive retraining. This paper introduces a novel self-learning AI framework that integrates continual learning with meta-learning techniques to enable adaptive decision-making. We compare our approach with traditional static models and reinforcement learning agents in applications such as stock market prediction, autonomous navigation, and real-time strategy games. Experimental results show that our model significantly improves learning efficiency while reducing performance degradation over time.
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