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深度强化学习综述 - ict.ac.cn

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40 Vol. 40 2017 CHINESE JOURNAL OF COMPUTERS Online Publishing (61472262, 61303108, 61373094, 61502323, 61502329) SYG201422, SYG201308 1969 (CCF) 1992 . , , 1985 , , , , agent . 1983 . 1992 . 1992 . 1991 . + 1)( 215006) 2)( 210000) 3 TP18 , , , , , , , ,2017.

Abstract Deep reinforcement learning (DRL) is a new research hotspot in the artificial intelligence community. By using a general-purpose form, DRL integrates the advantages of the perception of deep learning (DL) and the decision making of reinforcement learning (RL), and gains the output control directly based on raw inputs by the

  Learning, Deep, Reinforcement, Deep learning, Reinforcement learning, Deep reinforcement learning

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基于深度学习的推荐系统研究综述 - ict.ac.cn

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(Beijing Institute of Remote Sensing, Beijing . 100192) 2) (D. epartment of Computer Science and Technology, Tsinghua University, Beijing 100084) Abstract. With the ever-growing volume, complexity and dynamicity of online information, recommender

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DCTCP, and the most suitable traffic control algorithm for RDMA data center is DCQCN. Other researches require expensive custom hardware, which is difficult to deploy. (2) The traffic control technology is a technology of fair utilization of limited resources. Therefore, the performance of the technology can be improved

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cjc.ict.ac.cn

AlphaGo 主要采用价值网络（value networks） 来评估棋盘的位置，用策略网络（policy networks） 来选择下棋步法，这两种网络都是深层神经网络模 型，AlphaGo 所取得的成果是深度学习带来的人工 智能的又一次突破，这也说明了深度学习具有强大 的潜力。

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生成对抗网络及其在图像生成中的 ... - ict.ac.cn

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and computational challenges. At the same time, generative adversarial networks are the latest and most successful technology among generative models. Especially in terms of image generation, compared with other generation models, generative adversarial networks can not only avoid complicated calculations, but also generate better quality images.

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(e.g., dopamine-based reward learning, energy-based learning). Hence, it is powerful on spatially-temporal information representation, asynchronous processing of event-based information, and self-organized learning with dynamic topologies. SNN belongs to cross-discipline research areas of brain science and computer science.

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