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 a 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 rewards 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 the difference between reinforcement learning and other machine learning techniques is. 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.
What you will learn in this book:
- 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....
Listen to this book now and learn more about reinforcement learning with Python!