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Distributed Deep Reinforcement Learning:A Survey and a Multi-player Multi-agent Learning Toolbox
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作者 Qiyue Yin Tongtong Yu +6 位作者 Shengqi Shen Jun Yang Meijing Zhao wancheng ni Kaiqi Huang Bin Liang Liang Wang 《Machine Intelligence Research》 EI CSCD 2024年第3期411-430,共20页
With the breakthrough of AlphaGo,deep reinforcement learning has become a recognized technique for solving sequential decision-making problems.Despite its reputation,data inefficiency caused by its trial and error lea... With the breakthrough of AlphaGo,deep reinforcement learning has become a recognized technique for solving sequential decision-making problems.Despite its reputation,data inefficiency caused by its trial and error learning mechanism makes deep reinforcement learning difficult to apply in a wide range of areas.Many methods have been developed for sample efficient deep reinforcement learning,such as environment modelling,experience transfer,and distributed modifications,among which distributed deep reinforcement learning has shown its potential in various applications,such as human-computer gaming and intelligent transportation.In this paper,we conclude the state of this exciting field,by comparing the classical distributed deep reinforcement learning methods and studying important components to achieve efficient distributed learning,covering single player single agent distributed deep reinforcement learning to the most complex multiple players multiple agents distributed deep reinforcement learning.Furthermore,we review recently released toolboxes that help to realize distributed deep reinforcement learning without many modifications of their non-distributed versions.By analysing their strengths and weaknesses,a multi-player multi-agent distributed deep reinforcement learning toolbox is developed and released,which is further validated on Wargame,a complex environment,showing the usability of the proposed toolbox for multiple players and multiple agents distributed deep reinforcement learning under complex games.Finally,we try to point out challenges and future trends,hoping that this brief review can provide a guide or a spark for researchers who are interested in distributed deep reinforcement learning. 展开更多
关键词 Deep reinforcement learning distributed machine learning self-play population-play TOOLBOX
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Value of big data to finance:observations on an internet credit Service Company in China 被引量:2
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作者 Shaofeng Zhang Wei Xiong +1 位作者 wancheng ni Xin Li 《Financial Innovation》 2015年第1期259-276,共18页
Background:his paper presents a case study on 100Credit,an Internet credit service provider in China.100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating bus... Background:his paper presents a case study on 100Credit,an Internet credit service provider in China.100Credit began as an IT company specializing in e-commerce recommendation before getting into the credit rating business.The company makes use of Big Data on multiple aspects of individuals’online activities to infer their potential credit risk.Methods:Based on 100Credit’s business practices,this paper summarizes four aspects related to the value of Big Data in Internet credit services.Results:1)value from large data volume that provides access to more borrowers;2)value from prediction correctness in reducing lenders’operational cost;3)value from the variety of services catering to different needs of lenders;and 4)value from information protection to sustain credit service businesses.Conclusion:The paper also discusses the opportunities and challenges of Big Databased credit risk analysis,which needs to be improved in future research and practice. 展开更多
关键词 Big data Credit rating Information economics Value of information FINANCE
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人机对抗智能技术 被引量:28
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作者 黄凯奇 兴军亮 +2 位作者 张俊格 倪晚成 徐博 《中国科学:信息科学》 CSCD 北大核心 2020年第4期540-550,共11页
人机对抗作为人工智能研究的前沿方向,已成为国内外智能领域研究的热点,并为探寻机器智能内在生长机制和关键技术验证提供有效试验环境和途径.本文针对巨复杂、高动态、不确定的强对抗环境对智能认知和决策带来的巨大挑战,分析了人机对... 人机对抗作为人工智能研究的前沿方向,已成为国内外智能领域研究的热点,并为探寻机器智能内在生长机制和关键技术验证提供有效试验环境和途径.本文针对巨复杂、高动态、不确定的强对抗环境对智能认知和决策带来的巨大挑战,分析了人机对抗智能技术研究现状,梳理了其内涵和机理,提出了以博弈学习为核心的人机对抗智能理论研究框架;并在此基础上论述了其关键模型:对抗空间表示与建模、态势评估与推理、策略生成与优化、行动协同与控制;为复杂认知与决策问题的可建模、可计算、可解释求解奠定了基础.最后,本文总结了当前应用现状并对未来发展方向进行了展望. 展开更多
关键词 人工智能 人机对抗 机器学习 智能博弈 认知决策
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