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Training Artificial Neural Networks to Learn a Nondeterministic Game

机译:训练人工神经网络学习不确定性游戏

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It is well known that artificial neural networks (ANNs) can learn deterministic automata. Learning nondeterministic automata is another matter. This is important because much of the world is nondeterministic, taking the form of unpredictable or probabilistic events that must be acted upon. If ANNs are to engage such phenomena, then they must be able to learn how to deal with nondeterminism. In this project the game of Pong poses a nondeterministic environment. The learner is given an incomplete view of the game state and underlying deterministic physics, resulting in a nondeterministic game. Three models were trained and tested on the game: Mono, Elman, andNumenta's NuPIC.
机译:众所周知,人工神经网络(ANN)可以学习确定性自动机。学习不确定的自动机是另一回事。这很重要,因为世界上大部分地区都是不确定性的,采取必须采取的不可预测或概率性事件的形式。如果人工神经网络要参与这种现象,那么他们必须能够学习如何处理不确定性。在此项目中,Pong的游戏构成了不确定的环境。学习者对游戏状态和确定性物理学的了解不完整,从而导致了不确定性游戏。在游戏中对三种模型进行了训练和测试:Mono,Elman和Numenta的NuPIC。

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