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第13讲 智能Agent及多Agent系统
Chapter 13 Intelligent Agent & Multi-Agent Systems
徐从富
浙江大学人工智能研究所
-Article 13 stresses Intelligent Agent and Multi-Agent System Chapter 13 Intelligent Agent
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强化学习是人工智能中策略学习的一种，基于预期最大利益原则。和博弈论有密切的关系，也是多主体系统学习的常用方法。-Reinforcement learning is a kind of artificial intelligence in the strategic study, based on the principle of best interests is expected. And game theory are closely related, but also multi-agen
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非参数政策梯度的napping机制的java代码参考文献：Qing Da, Yang Yu, and Zhi-Hua Zhou. Napping for Functional Representation of Policy. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 14), Paris, France, 2014.-This
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基于迭代学习控制ILC的多智能体一致性跟踪研究，算法设计-Iterative learning control for multi-agent systems consensus tracking
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切换拓扑结构的多智能体系统编队控制算法,未知非线性系统，迭代学习控制算法-An iterative learning approach to formation control of multi-agent systems Switching topology
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粒子群优化在多智能体系统上的应用和仿真实现(the implementation of PSO in multi-agent systems)
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