Reinforcement Learning with Python

Reinforcement learning is one of those data science fields, which will most certainly shape the world. The changes are already visible since we have self-driving cars, robots and much more we used to see only in some futuristic movies. Reinforcement learning is widely used machine learning technique, a computational approach when it comes to the different software agents, which are trying to maximize the total amount of possible reward they receive while interacting with some uncertain as well as very complex environments.

This book is divided into seven chapters in which you will get to reinforcement techniques and methodology better. The first chapters will introduce you to the main concept laying being reinforcement learning techniques. Further, you will see what is the difference between reinforcement learning and other machine learning techniques. The book also provides some of the basic solution methods when it comes to the Markov decision processes, dynamic programming, Monte Carlo methods and temporal difference learning.

The book will definitely be your best companion as soon as you start working on your own reinforcement learning project in Python and you will realize that these learning techniques are our future. Even now, it is impossible to imagine our world without advancements in machine learning concept. The concept or reinforcement learning even though being present for many decades, has reached its peak only a couple of years ago. Many industries have been presenting amazingly innovative machines, robots and much that we saw only in the futuristic movies. I understand why this topic excites you, and if you have decent programming skills and of course a desire to embark on this adventure, the book will provide you an amazing start on your journey.

• Types of fundamental machine learning algorithms in comparison to reinforcement learning
• Essentials of reinforcement learning process
• Marko decision processes and basic parameters
• How to integrate reinforcement learning algorithm using OpenAI Gym
• How to integrate Monte Carlo methods for prediction
• Monte Carlo tree search
• Dynamic programming in Python for policy evaluation, policy iteration and value iteration
• Temporal difference learning or TD
• And much, much more...