Multi-Agent Machine Learning A Reinforcement Approach

主要责任者 Szepesvári, Csaba. 题名 Algorithms for reinforcement learning [electronic resource] / Csaba Szepesvári. 出版资料 San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2010. 摘要附注 Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.

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This is a draft by Dimitri P. Bertsekas, who from MIT. It may be published in 2019 by Athena Scientific. It is a good resource to study RL and Opt.

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David Silver的强化学习Reinforcement Learning课程讲义PPT 2017最新版

Practical Reinforcement Learning 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

2018.3月出版，完整326页，非预印版，找了很久的资料，分享给大家。

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