英文版PDF, 2018出版 Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to re inforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov’s Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI before looking at Open AI Gym. You’ll then learn about Swarm Intelligence with Python in terms of reinforcement learning. The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There’s also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google’s Deep Mind and see scenarios where reinforcement learning can be used. What You'll Learn Absorb the core concepts of the reinforcement learning process Use advanced topics of deep learning and AI Work with Open AI Gym, Open AI, and Python Harness reinforcement learning with TensorFlow and Keras using Python Who This Book Is For Data scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning. Table of Contents Chapter 1: Reinforcement Learning Basics Chapter 2: RL Theory and Algorithms Chapter 3: OpenAI Basics Chapter 4: Applying Python to Reinforcement Learning Chapter 5: Reinforcement Learning with Keras, TensorFlow, and ChainerRL Chapter 6: Google’s DeepMind and the Future of Reinforcement Learning

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.

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.

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

Nature资源，有关深度强化学习论文，可免费下载，资源共享

强化学习经典入门学习材料，每一个相关从业人员案头必备书籍

Sutton 的 An Introduction to Reinforcement Learning. 基础教程。

本书为Sutton的最新版的reinforcement learning：an introduction。

强化学习与最优控制；MIT；作者：Dimitri P. Bertsekas DRAFT TEXTBOOK；December 14, 2018