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reinforcement learning
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reinforcement learning
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一个多职能体的介绍资料,很好,值得学学,是英文资料,里面有例子-A Multi-agent Cooperative Reinforcement Learning
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Adaptive Fuzzy Function Approximation for Multi-Agent Reinforcement Learning
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强化学习在认知无线电领域的应用，很有价值的英文参考资料。-Reinforcement learning in the field of cognitive radio applications, valuable reference materials in English.
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安全加固 渗透测试 等等一系列的学习资料！内容比较全面-Security penetration testing, and so a series of reinforcement learning materials! More comprehensive content
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a pdf file about Online Clustering Algorithms and Reinforcement Learning using in onilne clustering
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QLearning algorithm - AI based reinforcement learning documentation for Java implementation-QLearning algorithm - AI based reinforcement learning documentation for Java implementation
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Paper Reinforcement learning with function approximation
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强化学习在数据中心资源节能管理中的研究，非常实用也比较新颖-Reinforcement Learning research in energy-saving management of data center resources
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多示例学习是与监督学习、非监督学习和强化学习并列的第四类学习框架，目前已广泛应用于药物设计、图像搜索等领域,并已获得很好的效果。在多示例学习中，训练样本是由多个示例组成的包，包是有概念标记的，但示例本身却没有概念标记，学习的目的是预测新包的类别。-Multi-instance learning and supervised learning, unsupervised learning and reinforcement learning tied for the fourth-class le
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matlab语句实现图模型，多用于分析网络用户产品推荐及网络用户潜在社会关系分析模型的建立。-The goal of building systems that can adapt to their environments and learn from
their experience has attracted researchers from many fields , including computer
science, engineering, mathematics, ph
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this good for student to learn reinforcement learning-this is good for student to learn reinforcement learning
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无线体域网中的功率控制，使用一种的方法RL，很新颖-Dynamic Power Control in
Wireless Body Area Networks
Using Reinforcement Learning With Approximation
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关于强化学习必不可少的论文，讲的很详细，希望对大家学习有利。-Papers about reinforcement learning is indispensable, very detailed, hope is good for everybody to learn
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Distributed Reinforcement Learning Based MAC
Protocols for Autonomous Cognitive Secondary
Users
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贝叶斯学习和强化学习相结合，其中包含贝叶斯Q学习-bayesian learning combined with reinforcement learning
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基于神经网络的强化学习是对强化学习算法的一种改进，本文讲述了将基于神经网络的强化学习算法用于移动机器人动态导航。-Reinforcement learning based on neural network is an improvement of reinforcement learning algorithm. This paper describes the reinforcement learning algorithm based on neural network for dynam
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the most official guide for reinforcement learning
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在这本书中，我们探索了一种从交互中学习的计算方法。我们不直接对人或动物如何学习进行理论分析，而是探索理想化的学习情境，评估各种学习方法的效率。也就是说，我们采用人工智能研究人员或工程师的角度。我们探索去设计在这些方面上格外有效率的机器，他能够解决科学或经济学领域的问题。通过数据分析和计算实验来评估这些设计。我们将这种方法称为强化学习，更侧重于目标导向的交互学习，而不是其他方法。(In this book, we explored a computational method of learnin
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