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与马尔理论相结合的强化学习

Reinforcement learning with Marr.

作者信息

Niv Yael, Langdon Angela

机构信息

Psychology Department & Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, 08540.

出版信息

Curr Opin Behav Sci. 2016 Oct;11:67-73. doi: 10.1016/j.cobeha.2016.04.005.

Abstract

To many, the poster child for David Marr's famous three levels of scientific inquiry is reinforcement learning-a computational theory of reward optimization, which readily prescribes algorithmic solutions that evidence striking resemblance to signals found in the brain, suggesting a straightforward neural implementation. Here we review questions that remain open at each level of analysis, concluding that the path forward to their resolution calls for inspiration across levels, rather than a focus on mutual constraints.

摘要

对许多人来说,大卫·马尔著名的三个科学探究层次的典型代表是强化学习——一种奖励优化的计算理论,它很容易给出算法解决方案,这些方案与大脑中发现的信号惊人地相似,这表明有一种直接的神经实现方式。在这里,我们回顾了在每个分析层次上仍然悬而未决的问题,得出结论:解决这些问题的前进道路需要跨层次的启发,而不是专注于相互约束。

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