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作品数：233被引量：393H指数：10
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Distributed Deep Reinforcement Learning:A Survey and a Multi-player Multi-agent Learning Toolbox
《Machine Intelligence Research》2024年第3期411-430,共20页Qiyue Yin Tongtong Yu Shengqi Shen Jun Yang Meijing Zhao Wancheng Ni Kaiqi Huang Bin Liang Liang Wang 
supported by Open Fund/Postdoctoral Fund of the Laboratory of Cognition and Decision Intelligence for Complex Systems,Institute of Automation,Chinese Academy of Sciences,China(No.CASIA-KFKTXDA27040809).
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...
关键词：Deep reinforcement learning distributed machine learning self-play population-play TOOLBOX 
Optimal Strategy for Aircraft Pursuit-evasion Games via Self-play Iteration
《Machine Intelligence Research》2024年第3期585-596,共12页Xin Wang Qing-Lai Wei Tao Li Jie Zhang 
In this paper,the pursuit-evasion game with state and control constraints is solved to achieve the Nash equilibrium of both the pursuer and the evader with an iterative self-play technique.Under the condition where th...
关键词：Differential games pursuit-evasion games nonlinear control optimal control Nash equilibrium solution 
AI in Human-computer Gaming:Techniques,Challenges and Opportunities被引量：2
《Machine Intelligence Research》2023年第3期299-317,共19页Qi-Yue Yin Jun Yang Kai-Qi Huang Mei-Jing Zhao Wan-Cheng Ni Bin Liang Yan Huang Shu Wu Liang Wang 
National Natural Science Foundation of China(No.61906197).
With the breakthrough of AlphaGo,human-computer gaming AI has ushered in a big explosion,attracting more and more researchers all over the world.As a recognized standard for testing artificial intelligence,various hum...
关键词：Human-computer gaming AI intelligent decision making deep reinforcement learning self-play 
Autonomous air combat decision-making of UAV based on parallel self-play reinforcement learning被引量：5
《CAAI Transactions on Intelligence Technology》2023年第1期64-81,共18页Bo Li Jingyi Huang Shuangxia Bai Zhigang Gan Shiyang Liang Neretin Evgeny Shouwen Yao 
National Natural Science Foundation of China,Grant/Award Number:62003267;Fundamental Research Funds for the Central Universities,Grant/Award Number:G2022KY0602;Technology on Electromagnetic Space Operations and Applications Laboratory,Grant/Award Number:2022ZX0090;Key Core Technology Research Plan of Xi'an,Grant/Award Number:21RGZN0016。
Aiming at addressing the problem of manoeuvring decision-making in UAV air combat,this study establishes a one-to-one air combat model,defines missile attack areas,and uses the non-deterministic policy Soft-Actor-Crit...
关键词：air combat decision deep reinforcement learning parallel self-play SAC algorithm UAV 
A Monte Carlo Neural Fictitious Self-Play approach to approximate Nash Equilibrium in imperfect-information dynamic games被引量：5
《Frontiers of Computer Science》2021年第5期137-150,共14页Li ZHANG Yuxuan CHEN Wei WANG Ziliang HAN Shijian Li Zhijie PAN Gang PAN 
National Key Research and Development Program of China(2017YFB1002503);Science and Technology Innovation 2030-“New Generation Artificial Intelligence”Major Project(2018AAA0100902),China.
Solving the optimization problem to approach a Nash Equilibrium point plays an important role in imperfect information games,e.g.,StarCraft and poker.Neural Fictitious Self-Play(NFSP)is an effective algorithm that lea...
关键词：approximate Nash Equilibrium imperfect-information games dynamic games Monte Carlo tree search Neural Fictitious Self-Play reinforcement learning 
Self-Play and Using an Expert to Learn to Play Backgammon with Temporal Difference Learning
《Journal of Intelligent Learning Systems and Applications》2010年第2期57-68,共12页Marco A. Wiering 
A promising approach to learn to play board games is to use reinforcement learning algorithms that can learn a game position evaluation function. In this paper we examine and compare three different methods for genera...
关键词：Board GAMES Reinforcement LEARNING TD(λ) Self-Play LEARNING From Demonstration 
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