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BEST Reinforcement Learning (RL) Books Update till Jan 2021

BEST Reinforcement Learning (RL) Books Update till Jan 2021

Reinforcement Learning is one of three basic Machine Learning paradigms, alongside supervised learning and unsupervised learning. For that, we have reviewed many books in this field. we present a selection of the best Reinforcement Learning books recently written by talented authors to include in our list. As an Amazon Associate, we earn a small commission from qualifying purchases, when you click

Reinforcement Learning is one of three basic Machine Learning paradigms, alongside supervised learning and unsupervised learning. For that, we have reviewed many books in this field. we present a selection of the best Reinforcement Learning books recently written by talented authors to include in our list.

As an Amazon Associate, we earn a small commission from qualifying purchases, when you click links on Cloudit-eg….. at no added cost to you.

 

1. Reinforcement Learning, second edition

An Introduction (Adaptive Computation and Machine Learning series) second edition

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field’s key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning’s relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson’s wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

 

 

2. Deep Reinforcement Learning Hands-On

Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition

by Maxim Lapan | January 31, 2020

New edition of the bestselling guide to deep reinforcement learning and how it’s used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more

Key Features

  • The second edition of the bestselling introduction to deep reinforcement learning expanded with six new chapters
  • Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods
  • Apply RL methods to cheap hardware robotics platforms

Book Description

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik’s Cube), multi-agent methods, Microsoft’s TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.

In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.

In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

What you will learn

  • Understand the deep learning context of RL and implement complex deep learning models
  • Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others
  • Build a practical hardware robot trained with RL methods for less than $100
  • Discover Microsoft’s TextWorld environment, which is an interactive fiction games platform
  • Use discrete optimization in RL to solve a Rubik’s Cube
  • Teach your agent to play Connect 4 using AlphaGo Zero
  • Explore the very latest deep RL research on topics including AI chatbots
  • Discover advanced exploration techniques, including noisy networks and network distillation techniques

Who this book is for

Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL.

3. Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning) 1st Edition

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 of 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.

4.  Deep Reinforcement Learning Hands-On

Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero, and more

by Maxim Lapan  | Jun 21, 2018

Publisher’s Note: This edition from 2018 is outdated and not compatible with any of the most recent updates to Python libraries. A new third edition, updated for 2020 with six new chapters that include multi-agent methods, discrete optimization, RL in robotics, and advanced exploration techniques is now available.

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.

Key Features

  • Explore deep reinforcement learning (RL), from the first principles to the latest algorithms
  • Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies, and genetic algorithms
  • Keep up with the very latest industry developments, including AI-driven chatbots

Book Description

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4.

The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on ‘grid world’ environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.

What you will learn

  • Understand the DL context of RL and implement complex DL models
  • Learn the foundation of RL: Markov decision processes
  • Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG, and others
  • Discover how to deal with discrete and continuous action spaces in various environments
  • Defeat Atari arcade games using the value iteration method
  • Create your own OpenAI Gym environment to train a stock trading agent
  • Teach your agent to play Connect4 using AlphaGo Zero
  • Explore the very latest deep RL research on topics including AI-driven chatbots

Who This Book Is For

Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.

5. Foundations of Deep Reinforcement Learning

Theory and Practice in Python (Addison-Wesley Data & Analytics Series)

by Laura Graesser, Wah Loon Keng  | Dec 9, 2019

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

6. Deep Reinforcement Learning A Complete Guide – 2020 Edition

by Gerardus Blokdyk| Sep 23, 2019

How can a better understanding of what is going on be obtained? Outside of work, who has had the greatest impact on your development and performance? What if you have a perfect model? How can an accurate picture of what is going on be obtained? Should you make this a high priority?

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There’s no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Deep Reinforcement Learning essentials are covered, from every angle: the Deep Reinforcement Learning self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Deep Reinforcement Learning outcomes are achieved.

Contains extensive criteria grounded in past and current successful projects and activities by experienced Deep Reinforcement Learning practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Deep Reinforcement Learning are maximized with professional results.

Your purchase includes access details to the Deep Reinforcement Learning self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria:

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Every self-assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature that allows you to receive verified self-assessment updates, ensuring you always have the most accurate information at your fingertips.

7. Reinforcement Learning A Complete Guide – 2021 Edition

by The Art of Service – Reinforcement Learning Publishing| Nov 10, 2020

Are the employee projects consistent with the expectations you communicated?

Can a manager use a directive style in a coaching consideration?

Can you apply the Big Idea to more than one instance or area?

Have you heard any negative feedback regarding your mobile supplier and mobile usage?

How can an accurate picture of what is going on be obtained?

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What types of negative feedback does your organization get from customers or competitors?

This Reinforcement Learning Guide is unlike books you’re used to. If you’re looking for a textbook, this might not be for you. This book and its included digital components are for you who understand the importance of asking great questions. This gives you the questions to uncover the Reinforcement Learning challenges you’re facing and generate better solutions to solve those problems.

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This Self-Assessment empowers people to do just that – whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO, etc… – they are the people who rule the future. They are the person who asks the right questions to make Reinforcement Learning investments work better.

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INCLUDES all the tools you need for an in-depth Reinforcement Learning Self-Assessment. Featuring new and updated case-based questions, organized into seven core levels of Reinforcement Learning maturity, this Self-Assessment will help you identify areas in which Reinforcement Learning improvements can be made.

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Your purchase includes access to the Reinforcement Learning self-assessment digital components which give you the dynamically prioritized projects-ready tool that enables you to define, show, and lead your organization exactly with what’s important.

8. Hands-On Reinforcement Learning with Python

Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

by Sudharsan Ravichandiran| June 28, 2018

A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python

Key Features

  • Enter the world of artificial intelligence using the power of Python
  • An example-rich guide to master various RL and DRL algorithms
  • Explore various state-of-the-art architectures along with math

Book Description

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence (AI). Hands-On Reinforcement Learning with Python will help you master not only basic reinforcement learning algorithms but also advanced deep reinforcement learning (DRL) algorithms.

The book starts with an introduction to reinforcement learning followed by OpenAI Gym and TensorFlow. You will then explore various RL algorithms and concepts, such as the Markov decision process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.

By the end of this book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.

What you will learn

  • Understand the basics of RL methods, algorithms, and elements
  • Train an agent to walk using OpenAI Gym and Tensorflow
  • Understand the Markov decision process, Bellman’s optimality, and temporal difference (TD) learning
  • Solve multi-armed bandit problems using various algorithms
  • Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
  • Build intelligent agents using the DRQN algorithm to play the Doom game
  • Teach agents to play the Lunar Lander game using DDPG
  • Train an agent to win a car racing game using dueling DQN

Who This Book Is For

Hands-On Reinforcement Learning with Python is for machine learning developers and deep learning enthusiasts interested in artificial intelligence and want to learn about reinforcement learning from scratch. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

9. Statistical Reinforcement Learning

by Masashi Sugiyama| June 30, 2020

Reinforcement learning is a mathematical framework for developing computer agents that can learn optimal behavior by relating generic reward signals with their 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 up-to-date 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 model-based and model-free 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 thought-provoking 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 state-of-the-art results, including dimensionality reduction in RL and risk-sensitive 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 graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.

10. Reinforcement Learning for Cyber-Physical Systems

with Cybersecurity Case Studies

by Chong Li, Meikang Qiu | Sep 30, 2020

 

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology, RL is one of the primary strands of machine learning. Different from other machine learning algorithms, such as supervised learning and unsupervised learning, the key feature of RL is its unique learning paradigm, i.e., trial-and-error. Combined with the deep neural networks, deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand, CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings, intelligent transportation, and electric grids.

However, the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms, or develop new RL algorithms to enable the real-time adaptive CPSs, remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms, each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques.

 

 

11. Deep Reinforcement Learning

Fundamentals, Research, and Applications 1st ed. 2020 Edition

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo.

Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance.Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations.

The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.

12. An Introduction to Deep Reinforcement Learning (Foundations and Trends(r) in Machine Learning)

Deep reinforcement learning is a combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.

Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research-level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms, and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications.

Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers, and students alike.

 

 

 

 

13. Deep Reinforcement Learning in Action 1st Edition

Summary

Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error.

About the book

Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym.

What’s inside

  • Building and training DRL networks
  • The most popular DRL algorithms for learning and problem solving
  • Evolutionary algorithms for curiosity and multi-agent learning
  • All examples available as Jupyter Notebooks

14. Reinforcement Learning

State-of-the-Art (Adaptation, Learning, and Optimization Book 12)

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization, and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.

The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation, and predictive state representations. Furthermore, topics such as transfer, evolutionary methods, and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, games, and computational neuroscience. In total, seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.

Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge
representation in reinforcement learning settings.

 

 

15. Reinforcement Learning and Optimal Control

This book considers large and challenging multistage decision problems, which can be solved in principle by dynamic programming, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance.

These methods are known by several essentially equivalent names: reinforcement learning, approximate dynamic programming, and neuro-dynamic programming. They underlie, among others, the recent impressive successes of self-learning in the context of games such as chess and Go.
One of the aims of the book is to explore the common boundary between artificial intelligence and optimal control and to form a bridge that is accessible by workers with a background in either field. Another aim is to organize coherently the broad mosaic of methods that have proved successful in practice while having a solid theoretical and/or logical foundation.
This may help researchers and practitioners to find their way through the maze of competing ideas that constitute the current state of the art. The mathematical style of this book is somewhat different than other books by the same author.
While we provide a rigorous, albeit short, a mathematical account of the theory of finite and infinite horizon dynamic programming, and some fundamental approximation methods, we rely more on intuitive explanations and less on proof-based insights. We also illustrate the methodology with many example algorithms and applications. Selected sections, instructional videos and slides, and other supporting material may be found at the author’s website.

 

 

16. Generative Deep Learning

Teaching Machines to Paint, Write, Compose, and Play

by David Foster  | Jul 16, 2019

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel in human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models.

Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.

  • Discover how variational autoencoders can change facial expressions in photos
  • Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation
  • Create recurrent generative models for text generation and learn how to improve the models using attention
  • Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting
  • Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

 

17. Reinforcement Learning

Industrial Applications of Intelligent Agents

by Phil Winder Ph. D.  | Dec 1, 2020

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids the constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself.

Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You’ll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn’t shy away from math and expects familiarity with ML.

  • Learn what RL is and how the algorithms help solve problems
  • Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning
  • Dive deep into a range of value and policy gradient methods
  • Apply advanced RL solutions such as meta-learning, hierarchical learning, multi-agent, and imitation learning
  • Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more
  • Get practical examples through the accompanying website

 

18. Multi-Agent Coordination: A Reinforcement Learning Approach (Wiley – IEEE)

Discover the latest developments in multi-robot coordination techniques with this insightful and original resource

Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with the minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms.

You’ll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field.

Readers will discover cutting-edge techniques for multi-agent coordination, including:

  • An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium
  • Improving the convergence speed of multi-agent Q-learning for cooperative task planning
  • Consensus Q-learning for multi-agent cooperative planning
  • The efficient computing of correlated equilibrium for cooperative q-learning-based multi-agent planning
  • A modified imperialist competitive algorithm for multi-agent stick-carrying applications

Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.

19. Deep Reinforcement Learning with Guaranteed Performance

A Lyapunov-Based Approach (Studies in Systems, Decision, and Control) 1st ed. 2020 Edition

This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances.

It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios.

The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators and the other centers on the effect of periodic input disturbance on redundancy resolution.

Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researches in the fields of adaptive and optimal control, robotics, and dynamic neural networks.

 

 

20. Mastering Reinforcement Learning with Python

Build next-generation self-learning models using reinforcement learning techniques and best practices

by Enes Bilgin  | Jan 11, 2021

Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices

Key Features

  • Understand how large-scale state-of-the-art RL algorithms and approaches work
  • Apply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more
  • Explore tips and best practices from experts that will enable you to overcome real-world RL challenges

Book Description

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.

Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you’ll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning.

As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls.

By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.

What you will learn

  • Model and solve complex sequential decision-making problems using RL
  • Develop a solid understanding of how state-of-the-art RL methods work
  • Use Python and TensorFlow to code RL algorithms from scratch
  • Parallelize and scale up your RL implementations using Ray’s RLlib package
  • Get in-depth knowledge of a wide variety of RL topics
  • Understand the trade-offs between different RL approaches
  • Discover and address the challenges of implementing RL in the real world

Who This Book Is For

This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.

21. Deep Reinforcement Learning with Python

Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow, 2nd Edition

by Sudharsan Ravichandiran  | Sep 30, 2020

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms

Key Features

  • Covers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithm
  • Learn how to implement algorithms with code by following examples with line-by-line explanations
  • Explore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrations

Book Description

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.

In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in-depth, demystifying the underlying math and demonstrating implementations through simple code examples.

The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.

By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.

What you will learn

  • Understand core RL concepts including the methodologies, math, and code
  • Train an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI Gym
  • Train an agent to play Ms Pac-Man using a Deep Q Network
  • Learn policy-based, value-based, and actor-critic methods
  • Master the math behind DDPG, TD3, TRPO, PPO, and many others
  • Explore new avenues such as the distributional RL, meta RL, and inverse RL
  • Use Stable Baselines to train an agent to walk and play Atari games

Who this book is for

If you’re a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you.

Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.

22. Simulation-Based Optimization

Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series Book 55)

by Abhijit Gosavi  | Sep 10, 2016

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.

Key features of this revised and improved Second Edition include:

· Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search, and meta-heuristics (simulated annealing, tabu search, and genetic algorithms)

· Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics

· An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: QLearningSARSA, and R-SMART algorithms, and policy search, via APIQPLearning, actor-critics, and learning automata

· A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two-time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations

Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning, and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science, and applied mathematics.

 

23. PyTorch 1.x Reinforcement Learning Cookbook

Over 60 recipes to design, develop, and deploy self-learning AI models using Python

by Yuxi (Hayden) Liu  | Oct 31, 2019

Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes

Key Features

  • Use PyTorch 1.x to design and build self-learning artificial intelligence (AI) models
  • Implement RL algorithms to solve control and optimization challenges faced by data scientists today
  • Apply modern RL libraries to simulate a controlled environment for your projects

Book Description

Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use.

With this book, you’ll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You’ll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cart pole problem using the multi-armed bandit algorithm and function approximation. You’ll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you’ll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game.

By the end of this book, you’ll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems.

What you will learn

  • Use Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problems
  • Develop a multi-armed bandit algorithm to optimize display advertising
  • Scale-up learning and control processes using Deep Q-Networks
  • Simulate Markov Decision Processes, OpenAI Gym environments, and other common control problems
  • Select and build RL models, evaluate their performance, and optimize and deploy them
  • Use policy gradient methods to solve continuous RL problems

Who this book is for

Machine learning engineers, data scientists, and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.

 

24. The Reinforcement Learning Workshop

Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide

Key Features

  • Use TensorFlow to write reinforcement learning agents for performing challenging tasks
  • Learn how to solve finite Markov decision problems
  • Train models to understand popular video games like Breakout

Book Description

Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.

Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem.

By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.

What you will learn

  • Use OpenAI Gym as a framework to implement RL environments
  • Find out how to define and implement reward function
  • Explore Markov chain, Markov decision process, and the Bellman equation
  • Distinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning
  • Understand the multi-armed bandit problem and explore various strategies to solve it
  • Build a deep Q model network for playing the video game Breakout

Who this book is for

If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

25. Grokking Deep Reinforcement Learning

by Miguel Morales  | Nov 10, 2020

Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.

Summary
We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You’ll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
We learn by interacting with our environment, and the rewards or punishments we experience guide our future behavior. Deep reinforcement learning brings that same natural process to artificial intelligence, analyzing results to uncover the most efficient ways forward. DRL agents can improve marketing campaigns, predict stock performance, and beat grandmasters in Go and chess.

About the book
Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.

What’s inside
An introduction to reinforcement learning
DRL agents with human-like behaviors
Applying DRL to complex situations

About the reader
For developers with a basic deep learning experience.

About the author
Miguel Morales works on reinforcement learning at Lockheed Martin and is an instructor for the Georgia Institute of Technology’s Reinforcement Learning and Decision Making course.

 

26. Deep Reinforcement Learning State of the art

by Youssef Fenjiro | Feb 13, 2019

Artificial intelligence has made big steps forward with reinforcement learning (RL) in the last century, and with the advent of deep learning (DL) in the 90s, especially,

the breakthrough of convolutional networks in the computer vision field. The adoption of DL neural networks in RL, in the first decade of the 21 century, led to an end-to-end framework allowing a great advance in human-level agents and autonomous systems, called deep reinforcement learning (DRL).
In this book, we will go through the development Timeline of RL and DL technologies, describing the main improvements made in both fields. Then, we will dive into DRL and have an overview of the state-of-the-art of this new and promising field, by browsing a set of algorithms (Value optimization, Policy optimization, and Actor-Critic), then, giving an outline of current challenges and real-world applications, along with the hardware and frameworks used.

 

 

 

 

 

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