Packt Publishing (November 6, 2017) ISBN: 978-1787128729 | 467 pages | PDF (conv) | 7 M

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.