没有梯度的De Novo学习
Karl Friston1,2, Thomas Parr3, Conor Heins2
1Queen Square Institute of Neurology, University College London, London WC1E 6BT, UK.
Entropy (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种新的积极推理方法,用于新的目标导向行为的学习. 该方法发现了政策优化的生成模型,使代理商能够从头开始学习最佳行动.
科学领域:
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 从头开始学习目标导向行为 (de novo学习) 是人工智能和神经科学的一个关键挑战.
- 当前的方法往往需要预先定义的模型或广泛的培训数据.
- 了解代理人如何在没有事先知识的情况下发现最佳政策对于开发更自主系统至关重要.
研究的目的:
- 应用主动推理来解决 de novo学习问题,以顺序优化政策.
- 开发一种程序,直接从观察中发现离散生成模型的结构和参数.
- 展示自由能源原理如何将学习重新定义为在生成模型动态中发现吸引集的发现.
主要方法:
- 积极推断框架应用于新的学习.
- 一个程序,增长和减少一个生成模型,以找到一个 pullback 吸引器在一般化状态.
- 使用自由能源原则重新构建学习问题.
- 与基于价值的公式进行比较,例如贝尔曼最佳性.
主要成果:
- 提出的方法成功地学习了一个具有吸引力的集合的生成模型,它代表了最少行动的路径.
- 这种吸引集引导代理人走向目标状态,同时避免昂贵的状态.
- 在模拟的街机游戏中展示了de novo结构学习和新兴机构.
结论:
- 积极推断为目标导向行为的新学习提供了一个有效的框架.
- 根据自由能源原则,可以将学习定义为在生成模型中发现有吸引力的集合.
- 这种方法有助于从观测数据中发现政策和代理行为.
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