NEURONLIKE ADAPTIVE ELEMENTS THAT CAN SOLVE DIFFICULT LEARNING CONTROL-PROBLEMS
NEURONLIKE ADAPTIVE ELEMENTS THAT CAN SOLVE DIFFICULT LEARNING CONTROL-PROBLEMS
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DOI：
10.1109/tsmc.1983.6313077
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发表时间：
1983-01-01
期刊：
影响因子：
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通讯作者：
ANDERSON, CW
中科院分区：
文献类型：
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作者：
BARTO, AG;SUTTON, RS;ANDERSON, CW
它示出了如何由两个神经元自适应元件组成的系统可以解决一个困难的学习控制问题。任务是平衡一个杆，这是铰接到一个可移动的车通过施加力到车的基础。有人认为，学习问题所面临的自适应元件，自适应网络的组成部分，至少是这个版本的极点平衡问题一样困难。学习系统由一个单一的关联搜索元素（ASE）和一个单一的自适应评论元素（ACE）。在学习平衡极点的过程中，ASE在强化反馈的影响下，通过搜索在输入和输出之间建立关联，而ACE则构建了比单独的强化反馈所能提供的信息量更大的评价函数。这种方法和其他尝试解决问题，使用neurolike元素之间的差异进行了讨论，因为是这项工作的关系，经典和工具的条件反射在动物学习研究和其可能的影响，在神经科学的研究。
It is shown how a system consisting of two neuronlike adaptive elements can solve a difficult learning control problem. The task is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. It is argued that the learning problems faced by adaptive elements that are components of adaptive networks are at least as difficult as this version of the pole-balancing problem. The learning system consists of a single associative search element (ASE) and a single adaptive critic element (ACE). In the course of learning to balance the pole, the ASE constructs associations between input and output by searching under the influence of reinforcement feedback, and the ACE constructs a more informative evaluation function than reinforcement feedback alone can provide. The differences between this approach and other attempts to solve problems using neurolike elements are discussed, as is the relation of this work to classical and instrumental conditioning in animal learning studies and its possible implications for research in the neurosciences.