PRACTICAL DEEP REINFORCEMENT LEARNING WITH PYTHON

Reinforcement learning is a fascinating branch of AI that differs from standard machine learning in several ways. Adaptation and learning in an unpredictable environment is the part of this project. There are numerous real-world applications for reinforcement learning these days, including medical, gambling, human imitation activity, and robotics.

The book brings a lot of innovative methods to the reader's attention in much practical learning, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical methods. While you understand these techniques in detail, the book also provides a real implementation of these methods and techniques using the power of TensorFlow and PyTorch. The book covers some enticing projects that show the power of reinforcement learning, and not to mention that everything is concise, up-to-date, and visually explained.

After finishing this book, the reader will have a thorough, intuitive understanding of modern reinforcement learning and its applications, which will tremendously aid them in delving into the interesting field of reinforcement learning.

What you will learn

● Familiarize yourself with the fundamentals of Reinforcement Learning and Deep Reinforcement Learning.

● Make use of Python and Gym framework to model an external environment.

● Apply classical Q-learning, Monte Carlo, Policy Gradient, and Thompson sampling techniques.

● Explore TensorFlow and PyTorch to practice the fundamentals of deep reinforcement learning.

● Design a smart agent for a particular problem using a specific technique.

This book is for machine learning engineers, deep learning fanatics, AI software developers, data scientists, and other data professionals eager to learn and apply Reinforcement Learning to ongoing projects. No specialized knowledge of machine learning is necessary; however, proficiency in Python is desired.