It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Foundations Of Deep Reinforcement Learning written by Laura Graesser and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-20 with Computers categories. An Introduction To Deep Reinforcement Learning written by Vincent Francois-Lavet and has been published by Foundations and Trends (R) in Machine Learning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-20 with categories. Foundation Of Deep Reinforcement Learning, Foundations Of Deep Reinforcement Learning, An Introduction To Deep Reinforcement Learning, Python Machine Learning – Second Edition: Machine Learning and Deep Learning with Python, scikit-lea, Outnumbered: From Facebook and Google to Fake News and Filter-bubbles – The Algorithms That Contro, Strategic Data Warehousing: Achieving Alignment with Business, Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, Pyomo ― Optimization Modeling in Python (Springer Optimization and Its Applications (67)), Data Science from Scratch: First Principles with Python, Practical SQL: A Beginner’s Guide to Storytelling with Data, Mastering PostgreSQL 12: Advanced techniques to build and administer scalable and reliable PostgreSQ, MySQL Cookbook: Solutions for Database Developers and Administrators, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, Programming with Microsoft Visual Basic 2017 (MindTap Course List), Network Security Through Data Analysis: From Data to Action, Data Analytics with Spark Using Python (Addison-Wesley Data & Analytics Series), Getting Started with SQL: A Hands-On Approach for Beginners, SAP S/4HANA Financial Accounting Certification Guide: Application Associate Exam (2nd Edition) (SAP, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, The Joy of PHP: A Beginner’s Guide to Programming Interactive Web Applications with PHP and mySQL, Computational Geometry: Algorithms and Applications, IC3 Certification Guide Using Microsoft Windows 10 & Microsoft Office 2016, Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. Foundations Of Deep Reinforcement Learning, An Introduction To Deep Reinforcement Learning, Exam Prep For Introduction To Biopsychology 9th Edition, foundations of deep reinforcement learning, the oxford handbook of new religious movements, m tter museum of the college of physicians of philadelphia, urban modernity in colonial korea and taiwan, nuevas perspectivas desde sobre am rica latina, a guide to nutrition and food service for nursing homes and homes for the aged, economic transition and political legitimacy in post mao china, assessment of democratic trends in nigeria, census of population 1960 classified index of occupations and industries. Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights, Artificial Intelligence: A Modern Approach, 4th Edition, Unlimited one-month access with your purchase, Corporate, Academic, and Employee Purchases, Mobile Application Development & Programming, How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning, Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertise, Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms, Includes case studies, practical tips, definitions, and other aids to learning and mastery, Prepares readers for exciting future advances in artificial general intelligence, Understand each key aspect of a deep RL problem, Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER), Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO), Understand how algorithms can be parallelized synchronously and asynchronously, Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work, Explore algorithm benchmark results with tuned hyperparameters, Understand how deep RL environments are designed, eBook (Watermarked)
Foundations Of Deep Reinforcement Learning written by Laura Graesser and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Artificial intelligence categories. Any errors that have been confirmed since this book was published can be downloaded below.
In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games–such as Go, Atari games, and DotA 2–to robotics.Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. Foundations of Deep Reinforcement Learning: Theory and Practice in Python [Rough Cuts] | Laura Graesser; Wah Loon Keng | download | B–OK. File Name : foundation-of-deep-reinforcement-learning.pdf Languange Used : English File Size : 44,8 Mb Total Download : 334 Download Now Read Online. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Get unlimited 30-day access to over 30,000 books about UX design, leadership, project management, teams, agile development, analytics, core programming, and so much more. Download Foundation Of Deep Reinforcement Learning PDF/ePub or read online books in Mobi eBooks. Deep Reinforcement Learning In Python written by Laura Harding Graesser and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-16 with Artificial intelligence categories. This site is like a library, Use search box in the widget to get ebook that you want. This eBook requires no passwords or activation to read. Copyright © 2020 IT eBooks Download All Rights Reserved. This eBook includes the following formats, accessible from your Account page after purchase: EPUB Reinforcement Learning - The Actor-Critic Algorithm, Foreword xixPreface xxiAcknowledgments xxvAbout the Authors xxviiChapter 1: Introduction to Reinforcement Learning 11.1 Reinforcement Learning 11.2 Reinforcement Learning as MDP 61.3 Learnable Functions in Reinforcement Learning 91.4 Deep Reinforcement Learning Algorithms 111.5 Deep Learning for Reinforcement Learning 171.6 Reinforcement Learning and Supervised Learning 191.7 Summary 21Part I: Policy-Based and Value-Based Algorithms 23Chapter 2: REINFORCE 252.1 Policy 262.2 The Objective Function 262.3 The Policy Gradient 272.4 Monte Carlo Sampling 302.5 REINFORCE Algorithm 312.6 Implementing REINFORCE 332.7 Training a REINFORCE Agent 442.8 Experimental Results 472.9 Summary 512.10 Further Reading 512.11 History 51Chapter 3: SARSA 533.1 The Q- and V-Functions 543.2 Temporal Difference Learning 563.3 Action Selection in SARSA 653.4 SARSA Algorithm 673.5 Implementing SARSA 693.6 Training a SARSA Agent 743.7 Experimental Results 763.8 Summary 783.9 Further Reading 793.10 History 79Chapter 4: Deep Q-Networks (DQN) 814.1 Learning the Q-Function in DQN 824.2 Action Selection in DQN 834.3 Experience Replay 884.4 DQN Algorithm 894.5 Implementing DQN 914.6 Training a DQN Agent 964.7 Experimental Results 994.8 Summary 1014.9 Further Reading 1024.10 History 102Chapter 5: Improving DQN 1035.1 Target Networks 1045.2 Double DQN 1065.3 Prioritized Experience Replay (PER) 1095.4 Modified DQN Implementation 1125.5 Training a DQN Agent to Play Atari Games 1235.6 Experimental Results 1285.7 Summary 1325.8 Further Reading 132Part II: Combined Methods 133Chapter 6: Advantage Actor-Critic (A2C) 1356.1 The Actor 1366.2 The Critic 1366.3 A2C Algorithm 1416.4 Implementing A2C 1436.5 Network Architecture 1486.6 Training an A2C Agent 1506.7 Experimental Results 1576.8 Summary 1616.9 Further Reading 1626.10 History 162Chapter 7: Proximal Policy Optimization (PPO) 1657.1 Surrogate Objective 1657.2 Proximal Policy Optimization (PPO) 1747.3 PPO Algorithm 1777.4 Implementing PPO 1797.5 Training a PPO Agent 1827.6 Experimental Results 1887.7 Summary 1927.8 Further Reading 192Chapter 8: Parallelization Methods 1958.1 Synchronous Parallelization 1968.2 Asynchronous Parallelization 1978.3 Training an A3C Agent 2008.4 Summary 2038.5 Further Reading 204Chapter 9: Algorithm Summary 205Part III: Practical Details 207Chapter 10: Getting Deep RL to Work 20910.1 Software Engineering Practices 20910.2 Debugging Tips 21810.3 Atari Tricks 22810.4 Deep RL Almanac 23110.5 Summary 238Chapter 11: SLM Lab 23911.1 Algorithms Implemented in SLM Lab 23911.2 Spec File 24111.3 Running SLM Lab 24611.4 Analyzing Experiment Results 24711.5 Summary 249Chapter 12: Network Architectures 25112.1 Types of Neural Networks 25112.2 Guidelines for Choosing a Network Family 25612.3 The Net API 26212.4 Summary 27112.5 Further Reading 271Chapter 13: Hardware 27313.1 Computer 27313.2 Data Types 27813.3 Optimizing Data Types in RL 28013.4 Choosing Hardware 28513.5 Summary 285Part IV: Environment Design 287Chapter 14: States 28914.1 Examples of States 28914.2 State Completeness 29614.3 State Complexity 29714.4 State Information Loss 30114.5 Preprocessing 30614.6 Summary 313Chapter 15: Actions 31515.1 Examples of Actions 31515.2 Action Completeness 31815.3 Action Complexity 31915.4 Summary 32315.5 Further Reading: Action Design in Everyday Things 324Chapter 16: Rewards 32716.1 The Role of Rewards 32716.2 Reward Design Guidelines 32816.3 Summary 332Chapter 17: Transition Function 33317.1 Feasibility Checks 33317.2 Reality Check 33517.3 Summary 337Epilogue 338Appendix A: Deep Reinforcement Learning Timeline 343Appendix B: Example Environments 345B.1 Discrete Environments 346B.2 Continuous Environments 350References 353Index 363.
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