<blockquote id="ad0y4" ></blockquote>

<blockquote id="ad0y4" ><meter id="ad0y4" ></meter></blockquote>

  • <source id="ad0y4" ></source>
  • <nobr id="ad0y4" ></nobr>

        <noscript id="ad0y4" ></noscript>
        <dl id="ad0y4" ><ins id="ad0y4" ><listing id="ad0y4" ></listing></ins></dl>

        <progress id="ad0y4" ><ol id="ad0y4" ></ol></progress>
      1. <dfn id="ad0y4" ></dfn>
          <optgroup id="ad0y4" ><dfn id="ad0y4" ><span id="ad0y4" ></span></dfn></optgroup>

          福彩手机客户端 > Store

          Foundations of Deep Reinforcement Learning: Theory and Practice in Python

          Register your product to gain access to bonus material or receive a coupon.

          Foundations of Deep Reinforcement Learning: Theory and Practice in Python

          Best Value Purchase

          Book + eBook Bundle

          • Your Price: $53.99
          • List Price: $89.98
          • Includes EPUB, MOBI, and PDF
          • About eBook Formats
          • This eBook includes the following formats, accessible from your Account page after purchase:

            ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

            MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

            Adobe Reader PDF The popular standard, used most often with the free software.

            This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

          More Purchase Options

          Book

          • Your Price: $39.99
          • List Price: $49.99
          • Usually ships in 24 hours.

          eBook (Watermarked)

          • Your Price: $31.99
          • List Price: $39.99
          • Includes EPUB, MOBI, and PDF
          • About eBook Formats
          • This eBook includes the following formats, accessible from your Account page after purchase:

            ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

            MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

            Adobe Reader PDF The popular standard, used most often with the free software.

            This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

          About

          Features

          • 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

          Description

          • 福彩手机客户端 2020
          • Dimensions: 7" x 9-1/8"
          • Pages: 416
          • Edition: 1st
          • Book
          • ISBN-10: 0-13-517238-1
          • ISBN-13: 978-0-13-517238-4

          The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice

          Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. 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. 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.
          This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.

          • 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
          Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

          Sample Content

          Online Sample Chapter

          Reinforcement Learning - The Actor-Critic Algorithm

          Table of Contents

          Foreword xix
          Preface xxi
          Acknowledgments xxv
          About the Authors xxvii


          Chapter 1: Introduction to Reinforcement Learning 1
          1.1 Reinforcement Learning 1
          1.2 Reinforcement Learning as MDP 6
          1.3 Learnable Functions in Reinforcement Learning 9
          1.4 Deep Reinforcement Learning Algorithms 11
          1.5 Deep Learning for Reinforcement Learning 17
          1.6 Reinforcement Learning and Supervised Learning 19
          1.7 Summary 21

          Part I: Policy-Based and Value-Based Algorithms 23
          Chapter 2: REINFORCE 25

          2.1 Policy 26
          2.2 The Objective Function 26
          2.3 The Policy Gradient 27
          2.4 Monte Carlo Sampling 30
          2.5 REINFORCE Algorithm 31
          2.6 Implementing REINFORCE 33
          2.7 Training a REINFORCE Agent 44
          2.8 Experimental Results 47
          2.9 Summary 51
          2.10 Further Reading 51
          2.11 History 51

          Chapter 3: SARSA 53
          3.1 The Q- and V-Functions 54
          3.2 Temporal Difference Learning 56
          3.3 Action Selection in SARSA 65
          3.4 SARSA Algorithm 67
          3.5 Implementing SARSA 69
          3.6 Training a SARSA Agent 74
          3.7 Experimental Results 76
          3.8 Summary 78
          3.9 Further Reading 79
          3.10 History 79

          Chapter 4: Deep Q-Networks (DQN) 81
          4.1 Learning the Q-Function in DQN 82
          4.2 Action Selection in DQN 83
          4.3 Experience Replay 88
          4.4 DQN Algorithm 89
          4.5 Implementing DQN 91
          4.6 Training a DQN Agent 96
          4.7 Experimental Results 99
          4.8 Summary 101
          4.9 Further Reading 102
          4.10 History 102

          Chapter 5: Improving DQN 103

          5.1 Target Networks 104
          5.2 Double DQN 106
          5.3 Prioritized Experience Replay (PER) 109
          5.4 Modified DQN Implementation 112
          5.5 Training a DQN Agent to Play Atari Games 123
          5.6 Experimental Results 128
          5.7 Summary 132
          5.8 Further Reading 132

          Part II: Combined Methods 133

          Chapter 6: Advantage Actor-Critic (A2C) 135

          6.1 The Actor 136
          6.2 The Critic 136
          6.3 A2C Algorithm 141
          6.4 Implementing A2C 143
          6.5 Network Architecture 148
          6.6 Training an A2C Agent 150
          6.7 Experimental Results 157
          6.8 Summary 161
          6.9 Further Reading 162
          6.10 History 162

          Chapter 7: Proximal Policy Optimization (PPO) 165
          7.1 Surrogate Objective 165
          7.2 Proximal Policy Optimization (PPO) 174
          7.3 PPO Algorithm 177
          7.4 Implementing PPO 179
          7.5 Training a PPO Agent 182
          7.6 Experimental Results 188
          7.7 Summary 192
          7.8 Further Reading 192

          Chapter 8: Parallelization Methods 195
          8.1 Synchronous Parallelization 196
          8.2 Asynchronous Parallelization 197
          8.3 Training an A3C Agent 200
          8.4 Summary 203
          8.5 Further Reading 204

          Chapter 9: Algorithm Summary 205

          Part III: Practical Details 207

          Chapter 10: Getting Deep RL to Work 209

          10.1 Software Engineering Practices 209
          10.2 Debugging Tips 218
          10.3 Atari Tricks 228
          10.4 Deep RL Almanac 231
          10.5 Summary 238

          Chapter 11: SLM Lab 239
          11.1 Algorithms Implemented in SLM Lab 239
          11.2 Spec File 241
          11.3 Running SLM Lab 246
          11.4 Analyzing Experiment Results 247
          11.5 Summary 249

          Chapter 12: Network Architectures 251
          12.1 Types of Neural Networks 251
          12.2 Guidelines for Choosing a Network Family 256
          12.3 The Net API 262
          12.4 Summary 271
          12.5 Further Reading 271

          Chapter 13: Hardware 273
          13.1 Computer 273
          13.2 Data Types 278
          13.3 Optimizing Data Types in RL 280
          13.4 Choosing Hardware 285
          13.5 Summary 285

          Part IV: Environment Design 287

          Chapter 14: States 289

          14.1 Examples of States 289
          14.2 State Completeness 296
          14.3 State Complexity 297
          14.4 State Information Loss 301
          14.5 Preprocessing 306
          14.6 Summary 313

          Chapter 15: Actions 315
          15.1 Examples of Actions 315
          15.2 Action Completeness 318
          15.3 Action Complexity 319
          15.4 Summary 323
          15.5 Further Reading: Action Design in Everyday Things 324

          Chapter 16: Rewards 327
          16.1 The Role of Rewards 327
          16.2 Reward Design Guidelines 328
          16.3 Summary 332

          Chapter 17: Transition Function 333
          17.1 Feasibility Checks 333
          17.2 Reality Check 335
          17.3 Summary 337

          Epilogue 338

          Appendix A: Deep Reinforcement Learning Timeline 343

          Appendix B: Example Environments 345

          B.1 Discrete Environments 346
          B.2 Continuous Environments 350

          References 353
          Index 363

          Updates

          Submit Errata

          More Information

          Unlimited one-month access with your purchase
          Free Safari Membership