面向计算机视觉和自然语言处理的深度强化学习研究进展

Caiming Xiong
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引用次数: 3

摘要

深度强化学习被认为是建立对世界有更高层次理解的自主系统的一种方式,将彻底改变人工智能领域。近年来,一些研究人员取得了许多进展,如学习玩雅达利等电子游戏,从摄像机输入学习机器人的控制策略。在本次演讲中,我们将从深度强化学习算法的一般介绍开始,包括策略优化,深度Qlearning,然后我们将重点介绍通过DRL在视觉和NLP方面取得的进展。
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Recent Progress in Deep Reinforcement Learning for Computer Vision and NLP
Deep reinforcement learning is considered as a way of building autonomous system with a higher level understanding of the world and would revolutionize the field of AI. Recently, some researchers have made many progresses such as learning to play video games like Atari, learning control policy for robots from camera input. In this talk, we begin with general introduction of deep reinforcement learning algorithms, including policy optimization, deep Qlearning, then we will highlight the progresses that have achieved in Vision and NLP via DRL.
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Kin-Verification Model on FIW Dataset Using Multi-Set Learning and Local Features RFIW 2017: LPQ-SIEDA for Large Scale Kinship Verification Session details: Keynote & Invited Talks Recent Progress in Deep Reinforcement Learning for Computer Vision and NLP KinNet
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