Practical Deep Reinforcement Learning with Python: Concise Implementation of Algorithms, Simplified Maths, and Effective Use of TensorFlow and PyTorch (English Edition)
Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow
KEY FEATURES
● Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical.
● Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects.
● Everything is concise, up-to-date, and visually explained with simplified mathematics.
DESCRIPTION
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.
This book introduces readers to reinforcement learning from a pragmatic point of view. The book does involve mathematics, but it does not attempt to overburden the reader, who is a beginner in the field of reinforcement learning.
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.
WHO THIS BOOK IS FOR
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.
AUTHOR BIO
Ivan Gridin is a researcher, author, developer, and artificial intelligence expert who has worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the primary areas of his research is the design and development of predictive time series models. Ivan has fundamental math skills in random process theory, time series analysis, machine learning, reinforcement learning, neural architecture search, and optimization. He has published books on genetic algorithms and time series forecasting.
He is a loving husband and father and collector of old math books.
Description:
Introducing Practical Smart Agents Development using Python, PyTorch, and TensorFlow
KEY FEATURES
● Exposure to well-known RL techniques, including Monte-Carlo, Deep Q-Learning, Policy Gradient, and Actor-Critical.
● Hands-on experience with TensorFlow and PyTorch on Reinforcement Learning projects.
● Everything is concise, up-to-date, and visually explained with simplified mathematics.
DESCRIPTION
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.
This book introduces readers to reinforcement learning from a pragmatic point of view. The book does involve mathematics, but it does not attempt to overburden the reader, who is a beginner in the field of reinforcement learning.
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.
WHO THIS BOOK IS FOR
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.
AUTHOR BIO
Ivan Gridin is a researcher, author, developer, and artificial intelligence expert who has worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the primary areas of his research is the design and development of predictive time series models. Ivan has fundamental math skills in random process theory, time series analysis, machine learning, reinforcement learning, neural architecture search, and optimization. He has published books on genetic algorithms and time series forecasting.
He is a loving husband and father and collector of old math books.