Reinforcement Learning (Paperback)

Phil Winder PH.D.

ISBN 10: 1098114833 / ISBN 13: 9781098114831

Published by O'Reilly Media, Sebastopol, 2020

About this Item

Paperback. Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to perform the reinforcement process that allows a machine to learn by itself.Author Dr. Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focusing on industrial applications, and learn numerous algorithms, frameworks, and environments. This is no cookbook-it doesn't shy away from math and expects familiarity with ML.Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into value methods and policy gradient methods Apply advanced RL implementations such as meta learning, hierarchical learning, evolutionary algorithms, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying Git repository" Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This practical book shows data science and AI professionals how to perform the reinforcement process that allows a machine to learn by itself. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781098114831

Bibliographic Details

• Title: Reinforcement Learning (Paperback)
• Publisher: O'Reilly Media, Sebastopol
• Publication Date: 2020
• Language: English
• ISBN 10: 1098114833
• ISBN 13: 9781098114831
• Binding: Paperback
• Condition: new

About this title

Synopsis

Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself.

Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML.

• Learn what RL is and how the algorithms help solve problems
• Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning
• Dive deep into a range of value and policy gradient methods
• Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning
• Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more
• Get practical examples through the accompanying website

About the Author

Dr. Phil Winder is a multidisciplinary Software Engineer and Data Scientist. As the CEO of Winder Research, a Cloud-Native Data Science consultancy based in the UK, he helps startups and enterprises utilise Data Science. Through a combination of consulting and development they are able to grow and scale their business by improving their products and platforms.

For the past 5 years, Phil has taught thousands of Engineers about Data Science in his range of Data Science training courses at conferences, in public, in private and on the online Safari learning platform. In these courses Phil focuses on the practicalities of using Data Science in industry on a wide range of topics from cleaning data all the way through to deep reinforcement learning.