Hands-On Reinforcement Learning with Python : Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow

Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.;Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Reinforcement Learning; What is RL?; RL algorithm; How RL differs from other ML paradigms; Elements of RL; Agent; Policy function; Value function; Model; Agent environment interface; Types of RL environment; Deterministic environment; Stochastic environment; Fully observable environment; Partially observable environment; Discrete environment; Continuous environment; Episodic and non-episodic environment; Single and multi-agent environment; RL platforms. Cover Title Page Copyright and Credits Dedication Packt Upsell Contributors Table of Contents Preface Chapter 1: Introduction to Reinforcement Learning What is RL? RL algorithm How RL differs from other ML paradigms Elements of RL Agent Policy function Value function Model Agent environment interface Types of RL environment Deterministic environment Stochastic environment Fully observable environment Partially observable environment Discrete environment Continuous environment Episodic and non-episodic environment Single and multi-agent environment RL platforms. OpenAI Gym and UniverseDeepMind Lab RL-Glue Project Malmo ViZDoom Applications of RL Education Medicine and healthcare Manufacturing Inventory management Finance Natural Language Processing and Computer Vision Summary Questions Further reading Chapter 2: Getting Started with OpenAI and TensorFlow Setting up your machine Installing Anaconda Installing Docker Installing OpenAI Gym and Universe Common error fixes OpenAI Gym Basic simulations Training a robot to walk OpenAI Universe Building a video game bot TensorFlow Variables, constants, and placeholders Variables. ConstantsPlaceholders Computation graph Sessions TensorBoard Adding scope Summary Questions Further reading Chapter 3: The Markov Decision Process and Dynamic Programming The Markov chain and Markov process Markov Decision Process Rewards and returns Episodic and continuous tasks Discount factor The policy function State value function State-action value function (Q function) The Bellman equation and optimality Deriving the Bellman equation for value and Q functions Solving the Bellman equation Dynamic programming Value iteration Policy iteration. Solving the frozen lake problemValue iteration Policy iteration Summary Questions Further reading Chapter 4: Gaming with Monte Carlo Methods Monte Carlo methods Estimating the value of pi using Monte Carlo Monte Carlo prediction First visit Monte Carlo Every visit Monte Carlo Let's play Blackjack with Monte Carlo Monte Carlo control Monte Carlo exploration starts On-policy Monte Carlo control Off-policy Monte Carlo control Summary Questions Further reading Chapter 5: Temporal Difference Learning TD learning TD prediction TD control Q learning. Solving the taxi problem using Q learningSARSA Solving the taxi problem using SARSA The difference between Q learning and SARSA Summary Questions Further reading Chapter 6: Multi-Armed Bandit Problem The MAB problem The epsilon-greedy policy The softmax exploration algorithm The upper confidence bound algorithm The Thompson sampling algorithm Applications of MAB Identifying the right advertisement banner using MAB Contextual bandits Summary Questions Further reading Chapter 7: Deep Learning Fundamentals Artificial neurons ANNs Input layer Hidden layer Output layer. A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key FeaturesYour entry point into the world of artificial intelligence using the power of PythonAn example-rich guide to master various RL and DRL algorithmsExplore various state-of-the-art architectures along with mathBook DescriptionReinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning 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 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 the 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 learnUnderstand the basics of reinforcement learning methods, algorithms, and elementsTrain an agent to walk using OpenAI Gym and TensorflowUnderstand the Markov Decision Process, Bellmans optimality, and TD learningSolve multi-armed-bandit problems using various algorithmsMaster deep learning algorithms, such as RNN, LSTM, and CNN with applicationsBuild intelligent agents using the DRQN algorithm to play the Doom gameTeach agents to play the Lunar Lander game using DDPGTrain an agent to win a car racing game using dueling DQNWho this book is forIf youre a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book. Table of ContentsIntroduction to Reinforcement LearningGetting started with OpenAI and TensorflowMarkov Decision process and Dynamic ProgrammingGaming with Monte Carlo Tree SearchTemporal Difference LearningMulti-Armed Bandit ProblemDeep Learning FundamentalsDeep Learning and ReinforcementPlaying Doom With Deep Recurrent Q NetworkAsynchronous Advantage Actor Critic NetworkPolicy Gradients and OptimizationCapstone Project Car Racing using DQNCurrent Research and Next Steps Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around youAbout This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no timeWho This Book Is ForThis book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on itIn DetailArtificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!Style and approachThis highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python About This Book • Your entry point into 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 Who This Book Is For If you're a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book. What You Will Learn • Understand the basics of reinforcement learning methods, algorithms, and elements • Train an agent to walk using OpenAI Gym and Tensorflow • Understand the Markov Decision Process, Bellman's optimality, and 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 In Detail Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning 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 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 the 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. Style and approach This is a hands-on book designed to further expand your machine learning skills by understanding reinforcement to deep reinforcement learning algorithms with applications in Python. Build Machine Learning models with a sound statistical understanding. About This Book Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn Understand the Statistical and Machine Learning fundamentals necessary to build models Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages Analyze the results and tune the model appropriately to your own predictive goals Understand the concepts of required statistics for Machine Learning Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models. Downloading the example code for this book. You can download the example code files for al.. 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It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! What You Will Learn: Realize different classification and regression techniques; Understand the concept of clustering and how to use it to automatically segment data; See how to build an intelligent recommender system; Understand logic programming and how to use it; Build automatic speech recognition systems; Understand the basics of heuristic search and genetic programming; Develop games using Artificial Intelligence; Learn how reinforcement learning works; Discover how to build intelligent applications centered on images, text, and time series data; See how to use deep learning algorithms and build applications based on it--Publisher website A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python 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 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. 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. Descripción del editor:"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 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" (Amazon)