对比专家指导与强化学习，发现人类反馈能提升智能系统学习效果。
Unveiling the Role of Expert Guidance: A Comparative Analysis of User-centered Imitation Learning and Traditional Reinforcement Learning
- 用人类示范引导学习，比传统强化学习更高效
- 专家指导显著提升模型鲁棒性，次优示范会降低性能
- 适合研究人机协同智能系统的学者参考
人类反馈在提升智能系统学习能力中起关键作用。本研究对比了模仿学习与传统强化学习在系统中的表现、鲁棒性及局限性。基于Unity平台的仿真环境，通过大量实验评估专家指导与次优示范对学习过程的影响。结果表明，人类在环反馈可显著提升学习效率与稳定性，但示范质量直接影响最终性能。研究为构建以人为本的人工智能提供了重要启示，推动复杂现实问题求解模型的发展。
原文摘要 · Abstract (English)
Integration of human feedback plays a key role in improving the learning capabilities of intelligent systems. This comparative study delves into the performance, robustness, and limitations of imitation learning compared to traditional reinforcement learning methods within these systems. Recognizing the value of human-in-the-loop feedback, we investigate the influence of expert guidance and suboptimal demonstrations on the learning process. Through extensive experimentation and evaluations conducted in a pre-existing simulation environment using the Unity platform, we meticulously analyze the effectiveness and limitations of these learning approaches. The insights gained from this study contribute to the advancement of human-centered artificial intelligence by highlighting the benefits and challenges associated with the incorporation of human feedback into the learning process. Ultimately, this research promotes the development of models that can effectively address complex real-world problems.
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