Practical Reinforcement Learning
暫譯: 實用強化學習

Dr. Engr. S.M. Farrukh Akhtar

  • 出版商: Packt Publishing
  • 出版日期: 2017-10-17
  • 售價: $1,880
  • 貴賓價: 9.5$1,786
  • 語言: 英文
  • 頁數: 336
  • 裝訂: Paperback
  • ISBN: 1787128725
  • ISBN-13: 9781787128729
  • 相關分類: ReinforcementDeepLearning
  • 無法訂購

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商品描述

Master different reinforcement learning techniques and their practical implementation using OpenAI Gym, Python and Java

About This Book

  • Take your machine learning skills to the next level with reinforcement learning techniques
  • Build automated decision-making capabilities in your systems
  • Cover Reinforcement Learning concepts, frameworks, algorithms, and more in detail

Who This Book Is For

Machine learning/AI practitioners, data scientists, data analysts, machine learning engineers, and developers who are looking to expand their existing knowledge to build optimized machine learning models, will find this book very useful.

What You Will Learn

  • Understand the basics of reinforcement learning methods, algorithms, and more, and the differences between supervised, unsupervised, and reinforcement learning
  • Master the Markov Decision Process math framework by building an OO-MDP Domain in Java
  • Learn dynamic programming principles and the implementation of Fibonacci computation in Java
  • Understand Python implementation of temporal difference learning
  • Develop Monte Carlo methods and various policies used to build a Monte Carlo simulator using Python
  • Understand Policy Gradient methods and policies applied in the reinforcement domain
  • Instill reinforcement methods in the autonomous platform using a moving car example
  • Apply reinforcement learning algorithms in games with REINFORCEjs

In Detail

Reinforcement learning (RL) is becoming a popular tool for constructing autonomous systems that can improve themselves with experience. We will break the RL framework into its core building blocks, and provide you with details of each element.

This book aims to strengthen your machine learning skills by acquainting you with reinforcement learning algorithms and techniques. This book is divided into three parts. The first part defines Reinforcement Learning and describes its basics. It also covers the basics of Python and Java frameworks, which we are going to use later in the book. The second part discusses learning techniques with basic algorithms such as Temporal Difference, Monte Carlo, and Policy Gradient—all with practical examples. Lastly, in the third part we apply Reinforcement Learning with the most recent and widely used algorithms via practical applications.

By the end of this book, you'll know the practical implementation of case studies and current research activities to help you advance further with Reinforcement Learning.

Style and approach

This hands-on book will further expand your machine learning skills by teaching you the different reinforcement learning algorithms and techniques using practical examples.

商品描述(中文翻譯)

**掌握不同的強化學習技術及其在 OpenAI Gym、Python 和 Java 中的實際應用**

## 本書介紹

- 透過強化學習技術提升您的機器學習技能
- 在您的系統中建立自動化決策能力
- 詳細涵蓋強化學習的概念、框架、演算法等

## 本書適合誰閱讀

本書非常適合機器學習/人工智慧從業者、資料科學家、資料分析師、機器學習工程師以及希望擴展現有知識以建立優化機器學習模型的開發者。

## 您將學到什麼

- 理解強化學習方法、演算法等的基本概念,以及監督式學習、非監督式學習和強化學習之間的差異
- 通過在 Java 中構建 OO-MDP 領域,掌握馬可夫決策過程的數學框架
- 學習動態規劃原則及在 Java 中實現 Fibonacci 計算
- 理解 Python 中的時間差學習實現
- 開發蒙地卡羅方法及使用 Python 構建蒙地卡羅模擬器的各種策略
- 理解在強化學習領域中應用的策略梯度方法和策略
- 使用移動汽車範例在自主平台中灌輸強化方法
- 在遊戲中應用強化學習演算法,使用 REINFORCEjs

## 詳細內容

強化學習(RL)正成為構建能夠隨著經驗自我改進的自主系統的熱門工具。我們將把 RL 框架拆解為其核心組成部分,並提供每個元素的詳細資訊。

本書旨在通過讓您熟悉強化學習演算法和技術來加強您的機器學習技能。本書分為三個部分。第一部分定義強化學習並描述其基本概念,還涵蓋了我們稍後將使用的 Python 和 Java 框架的基礎知識。第二部分討論學習技術,介紹基本演算法,如時間差、蒙地卡羅和策略梯度,並提供實際範例。最後,在第三部分,我們通過實際應用來應用強化學習中最新和廣泛使用的演算法。

在本書結束時,您將了解案例研究和當前研究活動的實際應用,幫助您在強化學習方面更進一步。

## 風格與方法

這本實用的書籍將通過實際範例教您不同的強化學習演算法和技術,進一步擴展您的機器學習技能。