论文标题
从心理好奇心到人工好奇心：人工智能任务中的好奇心驱动的学习
From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks
论文作者
论文摘要
心理好奇心在人类智力中起着重要的作用，可以通过探索和信息获取来增强学习。在人工智能（AI）社区中，人造好奇心为受到人类认知发展的启发提供了自然的内在动机。同时，它可以弥合AI研究和实际应用方案之间的现有差距，例如过度拟合，概括不良，培训样本有限，计算成本高。因此，好奇心驱动的学习（CDL）变得越来越流行，在代理商进行自我激励以学习新颖知识的情况下。在本文中，我们首先介绍了关于好奇心心理学研究的全面综述，并总结了一个统一的框架，以量化好奇心及其唤醒机制。根据心理原则，我们进一步调查了强化学习，建议和分类领域中现有CDL方法的文献，其中讨论了优势和缺点以及未来的工作。结果，这项工作为未来的CDL研究提供了富有成果的见解，并产生了可能的方向以进一步改进。
Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition. In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning as inspired by human cognitive development; meanwhile, it can bridge the existing gap between AI research and practical application scenarios, such as overfitting, poor generalization, limited training samples, high computational cost, etc. As a result, curiosity-driven learning (CDL) has become increasingly popular, where agents are self-motivated to learn novel knowledge. In this paper, we first present a comprehensive review on the psychological study of curiosity and summarize a unified framework for quantifying curiosity as well as its arousal mechanism. Based on the psychological principle, we further survey the literature of existing CDL methods in the fields of Reinforcement Learning, Recommendation, and Classification, where both advantages and disadvantages as well as future work are discussed. As a result, this work provides fruitful insights for future CDL research and yield possible directions for further improvement.
