Comparative Evaluation of Reinforcement Learning and Supervised Learning in Autonomous Systems
Abstract
Reinforcement learning (RL) has gained traction in autonomous decision-making, but how does it compare to traditional supervised learning in real-world applications? This paper conducts a comparative evaluation of RL and supervised learning models in autonomous robotics, self-driving cars, and financial trading systems. We analyze performance in terms of adaptability, computational complexity, and generalization across dynamic environments. The findings highlight scenarios where RL surpasses supervised learning, providing insights into optimizing AI-driven automation.
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