Combining Reinforcement Learning and Human Feedback for AI System Optimization

Abstract

The optimization of artificial intelligence (AI) systems has become increasingly complex due to the dynamic nature of real-world environments. This paper presents a novel approach that combines reinforcement learning (RL) with human feedback to enhance AI system performance. By integrating human insights into the RL process, we aim to address the limitations of traditional RL methods, which often struggle to learn efficiently in sparse reward environments. Our proposed framework utilizes human feedback to guide the exploration of the action space, improving the efficiency of learning and accelerating convergence to optimal solutions. We demonstrate the effectiveness of this hybrid approach through a series of experiments across various domains, including robotics and game playing. The results indicate a significant improvement in learning speed and overall performance compared to standard RL algorithms. This study highlights the potential of leveraging human expertise in the training of AI systems, paving the way for more robust and adaptive AI solutions in complex applications.