Reinforcement learning合集

this file contains:Advanced Deep Learning with Keras_ Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more (2018, Packt Publishing.pdf Deep Reinforcement Learning for Wireless Networks (2019, Springer International Publishing).pdf Deep Reinforcement Learning HandsOn_ Apply modern RL methods, with deep Qnetworks, value iteration, policy gradients, TRPO, AlphaGo Zero and more.pdf HandsOn Reinforcement Learning with Python_ Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow (2018, Packt Publishing).epub HandsOn Reinforcement Learning with Python_ Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow (2018, Packt Publishing).pdf Keras Reinforcement Learning Projects (2018, Packt Publishing).epub Keras Reinforcement Learning Projects (2018, Packt Publishing).pdf Practical Reinforcement Learning Develop selfevolving, intelligent agents with OpenAI Gym, Python and Java.pdf Python Reinforcement Learning Projects  2018.pdf Reinforcement Learning for Optimal Feedback Control (2018, Springer International Publishing).pdf Reinforcement Learning with TensorFlow_ A beginner’s guide to designing selflearning systems with TensorFlow and OpenAI Gym (2018, Packt Publishing).pdf Reinforcement Learning _ With Open AI, TensorFlow and Keras Using PythonApress (2018).pdf Reinforcement Learning_ An Introduction (2018, The MIT Press).pdf SimulationBased Optimization_ Parametric Optimization Techniques and Reinforcement Learning (2015, Springer US).pdf Statistics for Machine Learning_ Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and RPackt Publishing (2017).pdf Tensorflow for Deep Learning_ From Linear Regression to Reinforcement Learning (2018, O'Reilly Media).pdf
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Reinforcement Learning : With Open AI, TensorFlow and Keras Using Python
20180729Master 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 interrelated. You’ll then work with theories related to reinforcement 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.
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Reinforcement Learning_An Introduction多版本合集
20171205Reinforcement Learning_An Introduction.pdf完整多版本合集第二版
 17.94MB
Keras Reinforcement Learning Projects
20181022A practical guide to mastering reinforcement learning algorithms using Keras Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs humanlevel performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library. The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes. Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of QLearning and reinforcement learning algorithms. By the end of this book, you’ll not only have developed handson training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.
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Statistical Reinforcement Learning  Modern Machine Learning Approaches
19531114Statistical Reinforcement Learning: Modern Machine Learning Approaches Masashi Sugiyama Taylor & Francis, 16 Mar 2015  Business & Economics  206 pages Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data. Supplying an uptodate and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including modelbased and modelfree approaches, policy iteration, and policy search methods. Covers the range of reinforcement learning algorithms from a modern perspective Lays out the associated optimization problems for each reinforcement learning scenario covered Provides thoughtprovoking statistical treatment of reinforcement learning algorithms The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents stateoftheart results, including dimensionality reduction in RL and risksensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques. This book is an ideal resource for graduatelevel students in computer science and applied statistics programs, as well as researchers and engineers in related fields.
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Asynchronous Methods for Deep Reinforcement Learning
20180201增强学习 经典算法 A3C 论文摘要： We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actorlearners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actorcritic, surpasses the current stateoftheart on the Atari domain while training for half the time on a single multicore CPU instead of a GPU. Furthermore, we show that asynchronous actorcritic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.
 10.99MB
[PDF] Reinforcement Learning With Open AI, TensorFlow and Keras Using Python
20180120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 interrelated. 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
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Reinforcement Learning  An Introduction 2nd 2017
20170526Reinforcement Learning  An Introduction 2nd 2017
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reinforcement learning ：an introduction 2018最新版book pdf格式
20180423本书为Sutton的最新版的reinforcement learning：an introduction。
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Reinforcement Learning an introduction 2018 pdf
20181007Richard Sutton and Andrew Barto 的经典书籍《Reinforcement Learning an introduction》。阅读本书是学习强化学习之路上不可避免的一环。而本书是经过两次修改2018年的最新版本。同时推荐youtube上面Alpha Go项目组David Silver的教学视屏，搭配学习，效果更佳哦~
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Reinforcement Learning: An Introduction最新版习题解答（第一版本）
20171228Reinforcement Learning: An Introduction最新版习题解答
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Python Reinforcement Learning Projects  2018.pdf
20190722Can You Train a Dog to Sit? If so, You Can Master Reinforcement Learning in No time! Welcome to the world of reinforced learning. This is a world where selfdriving cars can be seen on real roads, where programs can beat world champions, where robots are not only a part of futuristic movies. Sound too scientifically involved for your expertise? Don?t worry; reinforcement learning is much more straightforward than you think. You do not need a college degree or to be a worldclass developer in order to build a reinforcement learning application. Some rudimentary Python programming skills and a basic knowledge of Machine Learning is all it takes for this book to turn you into an RL expert. By describing the concept of reinforcement learning in a simple, nontechnical way, teaching you its elements, applications, and algorithms in the most comprehensive way possible while giving you a great jumpingoff point with some amazing Python implementations, this book is a definite musthave for everyone who wants to master this popular branch of AI without drowning in the technical nonsense. Inside this Book You?ll Discover: The elements of reinforcement learning Reiniforcement Learning vs. other learning types Simulated evironments and Policies A guide to Markov Decision Processes Dynamic Programming An exploration of Monte Carlo Methods The secrets to Q Learning And much, much more! If this sounds like a good deal to you, read this book and become a Reinforcement Learning expert in a matter of days.
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Algorithms for Reinforcement Learning 等三本
20180926傅里叶变换及其应用（斯坦福大学） Algorithms for Reinforcement Learning SVD 及其应用（张志华）
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Algorithms for reinforcement learning
20171020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 longterm 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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Humanlevel control through deep reinforcement learning 理解
20171218Humanlevel control through deep reinforcement learning 理解
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Exploring Deep Reinforcement Learning with Multi QLearning.pdf
20191207深度强化学习最新经典论文 Exploring Deep Reinforcement Learning with Multi QLearning

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