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Titlebook: Reinforcement Learning From Scratch; Understanding Curren Uwe Lorenz Textbook 20221st edition The Editor(s) (if applicable) and The Author(

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發(fā)表于 2025-3-21 19:02:12 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Reinforcement Learning From Scratch
副標題Understanding Curren
編輯Uwe Lorenz
視頻videohttp://file.papertrans.cn/826/825936/825936.mp4
概述An introduction to reinforcement learning that is hands-on and accessible using Java and Greenfoot.Enables implementation of RL algorithms using easy-to-understand examples and implementations.Suitabl
圖書封面Titlebook: Reinforcement Learning From Scratch; Understanding Curren Uwe Lorenz Textbook 20221st edition The Editor(s) (if applicable) and The Author(
描述.In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work??..With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.K?lling, King‘s College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts.?.The result is?an accessible introduction into machine learning that? concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed?to play along, experiment, and add their own ideas and experiments.??
出版日期Textbook 20221st edition
關鍵詞Machine Learning; Artificial Intelligence; Reinforcement Learning; SARSA; Q-Learning; Policy Gadient; Acto
版次1
doihttps://doi.org/10.1007/978-3-031-09030-1
isbn_softcover978-3-031-09032-5
isbn_ebook978-3-031-09030-1
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 23:26:57 | 只看該作者
Uwe LorenzAn introduction to reinforcement learning that is hands-on and accessible using Java and Greenfoot.Enables implementation of RL algorithms using easy-to-understand examples and implementations.Suitabl
板凳
發(fā)表于 2025-3-22 01:08:12 | 只看該作者
978-3-031-09032-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Artificial Neural Networks as Estimators for State Values and the Action Selection,rticular, the so-called artificial neural networks are discussed. We will also learn possibilities to use such estimators to create parameterized policies which, for a given state, can produce and improve a useful probability distribution over the available actions.
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Basic Concepts of Reinforcement Learning,agent is and how it generates more or less intelligent behavior in an environment with its “policy.” The structure of the basic model of reinforcement learning is described and the concept of intelligence in terms of individual utility maximization is introduced. In addition, some formal means are i
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