神经元级预测和噪音可以实现灵活的寻求奖励的行为
Chenguang Li1, Jonah Brenner2, Adam Boesky3
1Biophysics Program, Harvard College, Cambridge, MA 02138.
bioRxiv : the preprint server for biology
|June 3, 2024
概括
神经网络表现出自主寻求奖励的行为,使用内部噪音和本地更新,适应没有外部信号的环境. 这种生物学上可信的方法使灵活的,自主管理的勘探和开发战略成为可能.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的强化学习通常依赖于明确的环境奖励功能.
- 自主代理需要适应性行为和决策的机制.
- 了解生物可信的学习规则对于推进人工智能至关重要.
研究的目的:
- 为了证明神经网络可以在没有外部奖励的情况下实现寻求奖励的行为.
- 研究内部噪音和局部更新在自主行为中的作用.
- 探索网络如何适应环境和建筑变化.
主要方法:
- 开发神经网络,利用本地预测更新和内部噪声.
- 分析控制探索-开发切换的吸引力动态.
- 测试网络适应架构,环境和电机接口修改的适应性.
- 调查任务偏好形成和偏见机制.
主要成果:
- 神经网络成功地实现了自主寻求奖励的行为.
- 内部噪音和局部更新足以进行适应性交互.
- 网络表现出可塑性,适应变化而不需要外部控制.
- 任务偏好被噪音,初始化和网络架构所影响.
结论:
- 一个新的,生物学上可信的算法可以实现与环境的自主,适应性互动.
- 这种方法消除了对明确环境奖励功能的需求.
- 这项工作为开发自治智能代理提供了灵活的框架.
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