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.

本文综述了心理好奇心对于人类智能的重要作用，阐述了人工好奇心与人类认知发展的联系及其应用场景，并对强化学习、推荐系统和分类方法等领域中已有的好奇心驱动学习方法进行综述，提出了深入研究的方向和未来的改进工作。

从心理好奇到人工好奇：人工智能任务中基于好奇心的学习

From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven Learning in Artificial Intelligence Tasks

World models are self-supervised predictive models of how the world evolves.
Humans learn world models by curiously exploring their environment, in the
process acquiring compact abstractions of high bandwidth sensory inputs, the
ability to plan across long temporal horizons, and an understanding of the
behavioral patterns of other agents. In this work, we study how to design such
a curiosity-driven Active World Model Learning (AWML) system. To do so, we
construct a curious agent building world models while visually exploring a 3D
physical environment rich with distillations of representative real-world
agents. We propose an AWML system driven by $\gamma$-Progress: a scalable and
effective learning progress-based curiosity signal. We show that
$\gamma$-Progress naturally gives rise to an exploration policy that directs
attention to complex but learnable dynamics in a balanced manner, thus
overcoming the "white noise problem". As a result, our $\gamma$-Progress-driven
controller achieves significantly higher AWML performance than baseline
controllers equipped with state-of-the-art exploration strategies such as
Random Network Distillation and Model Disagreement.

本文研究如何设计这样的好奇心驱动的主动世界模型学习系统，提出一种由可扩展和有效的学习进步基础好奇心信号驱动的 AWML 系统，并表明相对于基线控制器，该控制器的性能得到显着提高。