潜在学习进步驱动自主目标选择在人类强化学习学习中的自主目标选择
Gaia Molinaro1, Cédric Colas2, Pierre-Yves Oudeyer3
1University of California, Berkeley.
Advances in neural information processing systems
|December 8, 2025
概括
人类根据潜伏的学习进度选择目标,这是对学习的内部估计,而不仅仅是性能变化. 这一发现提升了对人类目标选择和人工智能发展的理解.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 人类是目标导向的代理人,但目标选择的机制尚未完全理解.
- 学习进度或绩效变化是人类和人工智能的目标选择的一个已知的因素.
- 内在或潜伏的学习进步在人类目标选择中的作用需要进一步调查.
研究的目的:
- 调查潜学习进度对人类目标选择的影响.
- 探索个人如何在没有立即绩效反的情况下估计学习.
- 识别导致目标选择中个体间差异的因素.
主要方法:
- 为人类参与者 (N = 175) 设计了一个层次化的强化学习任务.
- 参与者反复选择自己的目标,并学习目标条件的政策.
- 用行为数据和计算建模来分析目标选择策略.
主要成果:
- 潜在学习的进展显著影响了人类的目标选择.
- 在目标选择中观察到个体间的差异,并通过环境结构识别部分解释.
- 参与者可以在内部估计学习进度,独立于立即的绩效变化.
结论:
- 潜在的学习进步是人类目标选择的关键驱动力,补充了观察到的表现.
- 了解潜在学习可以导致个性化的学习系统和更类似人类的人工智能.
- 在识别环境结构的个人差异影响目标选择策略.
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