深度学习方法在序列博弈中的应用

V. Markova, V. Shopov
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引用次数: 0

摘要

本文重点研究了深度学习方法在序列博弈中的应用。主要假设是序列到序列学习方法是适用的,并且表现出比经典强化学习更好的性能。因此,自主智能体通过序列到序列的方法,能够在动态环境中通过学习发现手头问题的较好的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Deep Learning Approach in Sequential Games
This article emphases on the application of deep learning methods in sequential games. The main hypothesis is that sequence to sequence learning approach is applicable and demonstrates better performance than classic reinforcement learning. So autonomous agents trough sequence to sequence approach are able to discover good solutions to the problem at hand by learning in dynamic environment.
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