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Collaborative Learning of Human and Computer: Supervised Actor-Critic based Collaboration Scheme

机译:人力与计算机的协作学习:受监管演员 - 批评的协作计划

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Recent large-scale neural networks show a high performance to complex recognition tasks but to get such ability, it needs a huge number of learning samples and iterations to optimize it's internal parameters. However, under unknown environments, learning samples do not exist. In this paper, we aim to overcome this problem and help improve the learning capability of the system by sharing data between multiple systems. To accelerate the optimization speed, the novel system forms a collaboration with human and reinforcement learning neural network and for data sharing between systems to develop a super neural network.
机译:最近的大型神经网络对复杂的识别任务显示出高性能,但要获得这种能力,它需要大量的学习样本和迭代来优化它的内部参数。但是,在未知的环境下,学习样本不存在。在本文中,我们的目标是克服这个问题,通过在多个系统之间共享数据来帮助改善系统的学习能力。为了加速优化速度,新颖系统与人类和强化学习神经网络的合作形成,并且用于系统之间共享以开发超神经网络。

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