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.