Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices (Paperback)

Bilgin, Enes

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商品描述

Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices

 

Key Features:

  • Understand how large-scale state-of-the-art RL algorithms and approaches work
  • Apply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and more
  • Explore tips and best practices from experts that will enable you to overcome real-world RL challenges

 

Book Description:

Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.

 

Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning.

 

techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning.

 

As you advance, you'll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray's RLlib package. You'll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls.

 

By the end of this book, you'll have mastered how to train and deploy your own RL agents for solving RL problems.

 

What You Will Learn:

  • Model and solve complex sequential decision-making problems using RL
  • Develop a solid understanding of how state-of-the-art RL methods work
  • Use Python and TensorFlow to code RL algorithms from scratch
  • Parallelize and scale up your RL implementations using Ray's RLlib package
  • Get in-depth knowledge of a wide variety of RL topics
  • Understand the trade-offs between different RL approaches
  • Discover and address the challenges of implementing RL in the real world

 

Who This Book Is For:

This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.

商品描述(中文翻譯)

這本書將帶領讀者進入創建最先進的強化學習代理人的實踐領域,使用TensorFlow和RLlib解決複雜的現實世界商業和行業問題,並提供專家提示和最佳實踐。

重點特色:
- 瞭解大規模最先進的強化學習算法和方法的運作原理
- 應用強化學習解決市場營銷、機器人技術、供應鏈、金融、網絡安全等複雜問題
- 探索專家的提示和最佳實踐,幫助您克服現實世界的強化學習挑戰

書籍描述:
強化學習(RL)是一個用於創建自主學習代理人的人工智能(AI)領域。本書以堅實的理論基礎為基礎,採用實踐方法,並使用受現實世界行業問題啟發的示例,教授您最先進的RL知識。

從賭徒問題、馬爾可夫決策過程和動態規劃開始,本書深入介紹了傳統RL技術,如蒙特卡洛方法和時間差分學習。之後,您將學習深度Q學習、策略梯度算法、演員-評論家方法、基於模型的方法和多智能體強化學習。然後,您將介紹一些最成功的RL實現背後的關鍵方法,例如領域隨機化和好奇心驅動學習。

隨著進一步的學習,您將使用現代Python庫(如TensorFlow和Ray的RLlib套件)探索許多新穎的算法和高級實現。您還將了解如何在機器人技術、供應鏈管理、市場營銷、金融、智慧城市和網絡安全等領域實施RL,同時評估不同方法之間的權衡和避免常見問題。

通過閱讀本書,您將掌握如何訓練和部署自己的RL代理以解決RL問題。

您將學到什麼:
- 使用RL模型解決複雜的序列決策問題
- 建立對最先進RL方法運作原理的扎實理解
- 使用Python和TensorFlow從頭編寫RL算法
- 使用Ray的RLlib套件並行化和擴展RL實現
- 深入了解各種RL主題
- 理解不同RL方法之間的權衡
- 發現並解決在現實世界中實施RL的挑戰

本書適合專業的機器學習從業者和研究人員,他們希望通過在實際項目中實施先進的深度強化學習概念,專注於實踐強化學習。強化學習專家也可以通過這本書提升他們的知識,解決大規模和複雜的序列決策問題。需要具備Python編程和深度學習的工作知識,以及強化學習的先前經驗。