Practical Reinforcement Learning

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

Table of Contents

Chapter 1. Reinforcement Learning
Chapter 2. Markov Decision Process
Chapter 3. Dynamic Programming
Chapter 4. Temporal Difference Learning
Chapter 5. Monte Carlo Methods
Chapter 6. Learning And Planning
Chapter 7. Deep Reinforcement Learning
Chapter 8. Game Theory
Chapter 9. Reinforcement Learning Showdown
Chapter 10. Applications And Case Studies – Reinforcement Learning
Chapter 11. Current Research – Reinforcement Learning
Chapter 12. Article