Practical reinforcement learning : develop self-evolving, intelligent agents with OpenAI Gym, Python, and Java

Master different reinforcement learning techniques and their practical implementation using OpenAI Gym, Python and Java About This Book Take your machine learning skills to the next level with reinforcement learning techniques Build automated decision-making capabilities in your systems Cover Reinforcement Learning concepts, frameworks, algorithms, and more in detail Who This Book Is For Machine learning/AI practitioners, data scientists, data analysts, machine learning engineers, and developers who are looking to expand their existing knowledge to build optimized machine learning models, will find this book very useful. What You Will Learn Understand the basics of reinforcement learning methods, algorithms, and more, and the differences between supervised, unsupervised, and reinforcement learning Master the Markov Decision Process math framework by building an OO-MDP Domain in Java Learn dynamic programming principles and the implementation of Fibonacci computation in Java Understand Python implementation of temporal difference learning Develop Monte Carlo methods and various policies used to build a Monte Carlo simulator using Python Understand Policy Gradient methods and policies applied in the reinforcement domain Instill reinforcement methods in the autonomous platform using a moving car example Apply reinforcement learning algorithms in games with REINFORCEjs In Detail Reinforcement learning (RL) is becoming a popular tool for constructing autonomous systems that can improve themselves with experience. We will break the RL framework into its core building blocks, and provide you with details of each element. This book aims to strengthen your machine learning skills by acquainting you with reinforcement learning algorithms and techniques. This book is divided into three parts. The first part defines Reinforcement Learning and describes its basics. It also covers the basics of Python and Java frameworks, which we are going to use later in the book. The second part discusses learning techniques with basic algorithms such as Temporal Difference, Monte Carlo, and Policy Gradient—all with practical examples. Lastly, in the third part we apply Reinforcement Learning with the most recent and widely used algorithms via practical applications. By the end of this book, you'll know the practical implementation of case studies and current research activities to help you advance further with Reinforcement Learning. Style and approach This hands-on book will further expand your machine learning skills by teaching you the different reinforcement learning algorithms and techniques using practical examples. ""Cover "" ""Copyright"" ""Credits"" ""About the Author"" ""About the Reviewers"" ""www.PacktPub.com"" ""Customer Feedback"" ""Table of Contents"" ""Preface"" ""Chapter 1: Reinforcement Learning "" ""Overview of machine learning"" ""What is machine learning?"" ""Speech conversion from one language to another"" ""Suspicious activity detection from CCTVs"" ""Medical diagnostics for detecting diseases"" ""Supervised learning"" ""Unsupervised learning"" ""Reinforcement learning"" ""Introduction to reinforcement learning"" ""Positive reinforcement learning"" ""Negative reinforcement learning""""Applications of reinforcement learning"" ""Self-driving cars"" ""Drone autonomous aerial taxi"" ""Aerobatics autonomous helicopter"" ""TD-Gammon â#x80 #x93 computer game"" ""AlphaGo"" ""The agent environment setup"" ""Exploration versus exploitation"" ""Neural network and reinforcement learning"" ""Reinforcement learning frameworks/toolkits"" ""OpenAI Gym"" ""Getting Started with OpenAI Gym"" ""Docker"" ""Docker installation on Windows environment"" ""Docker installation on a Linux environment"" ""Running an environment"" ""Brown-UMBC Reinforcement Learning and Planning""""Walkthrough with Hello GridWorld"" ""Hello GridWorld project"" ""Summary"" ""Chapter 2: Markov Decision Process "" ""Introduction to MDP"" ""State"" ""Action"" ""Model"" ""Reward"" ""Policy"" ""MDP -- more about rewards"" ""Optimal policy"" ""More about policy"" ""Bellman equation"" ""A practical example of building an MDP domain"" ""GridWorld"" ""Terminal states"" ""Java interfaces for MDP definitions"" ""Single-agent domain"" ""State"" ""Action"" ""Action type"" ""SampleModel"" ""Environment"" ""EnvironmentOutcome"" ""TransitionProb""""Defining a GridWorld state"" ""Defining a GridWorld model"" ""Creating the state visualizer"" ""Testing it out"" ""Markov chain"" ""Building an object-oriented MDP domain"" ""Summary"" ""Chapter 3: Dynamic Programming "" ""Learning and planning"" ""Evaluating a policy"" ""Value iteration"" ""Value iteration implementation using BURLAP"" ""Output of the value iteration"" ""Policy iteration"" ""Bellman equations"" ""The relationship between Bellman equations"" ""Summary"" ""Chapter 4: Temporal Difference Learning "" ""Introducing TD learning"" ""TD lambda""""Estimating from data"" ""Learning rate"" ""Properties of learning rate"" ""Overview of TD(1)"" ""An example of TD(1)"" ""Why TD(1) is wrong"" ""Overview of TD(0)"" ""TD lambda rule"" ""K-step estimator"" ""Relationship between k-step estimators and TD lambda"" ""Summary"" ""Chapter 5: Monte Carlo Methods "" ""Monte Carlo methods"" ""First visit Monte Carlo"" ""Example â#x80 #x93 Blackjack"" ""Objective of the game"" ""Card scoring/values"" ""The deal"" ""Naturals"" ""The gameplay"" ""Applying the Monte Carlo approach"" ""Blackjack game implementation""