Reinforcement Learning

Description

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 reinforcementand 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, learnnumerous 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

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Other editions

• 

  Reinforcement Learning: Optimal Feedback Control with Industrial Applications

  • 2023-07-24
  • 318 pages
  • Ebook
  • Springer Nature
• 

  Reinforcement Learning: An Introduction

  • 2018-11-13
  • 552 pages
  • Paperback
  • A Bradford Book
• 

  Reinforcement Learning: State-of-the-Art

  • 2012-03-05
  • 638 pages
  • Ebook
  • Springer Science & Business Media
• 

  Reinforcement Learning: An Introduction

  • 1998-02-26
  • 356 pages
  • Paperback
  • MIT Press

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