Reinforcement Learning: Industrial Applications of Intelligent Agents
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
1143209368
Reinforcement Learning: Industrial Applications of Intelligent Agents
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
65.99 In Stock
Reinforcement Learning: Industrial Applications of Intelligent Agents

Reinforcement Learning: Industrial Applications of Intelligent Agents

by Phil Winder
Reinforcement Learning: Industrial Applications of Intelligent Agents

Reinforcement Learning: Industrial Applications of Intelligent Agents

by Phil Winder

Paperback

$65.99 
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Overview

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

Product Details

ISBN-13: 9781098114831
Publisher: O'Reilly Media, Incorporated
Publication date: 12/15/2020
Pages: 405
Product dimensions: 7.00(w) x 9.19(h) x (d)

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.

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