Keras Reinforcement Learning Projects (Paperback)

Giuseppe Ciaburro

  • 出版商: Packt Publishing
  • 出版日期: 2018-09-28
  • 售價: $2,130
  • 貴賓價: 9.5$2,024
  • 語言: 英文
  • 頁數: 288
  • 裝訂: Paperback
  • ISBN: 1789342090
  • ISBN-13: 9781789342093
  • 相關分類: DeepLearningReinforcement
  • 海外代購書籍(需單獨結帳)

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商品描述

A practical guide to mastering reinforcement learning algorithms using Keras

Key Features

  • Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into action
  • Get to grips with Keras and practice on real-world unstructured datasets
  • Uncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learning

Book Description

Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library.

The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You'll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You'll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes.

Once you've understood the basics, you'll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you'll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms.

By the end of this book, you'll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.

What you will learn

  • Practice the Markov decision process in prediction and betting evaluations
  • Implement Monte Carlo methods to forecast environment behaviors
  • Explore TD learning algorithms to manage warehouse operations
  • Construct a Deep Q-Network using Python and Keras to control robot movements
  • Apply reinforcement concepts to build a handwritten digit recognition model using an image dataset
  • Address a game theory problem using Q-Learning and OpenAI Gym

Who this book is for

Keras Reinforcement Learning Projects is for you if you are data scientist, machine learning developer, or AI engineer who wants to understand the fundamentals of reinforcement learning by developing practical projects. Sound knowledge of machine learning and basic familiarity with Keras is useful to get the most out of this book

Table of Contents

  1. Overview of Keras Reinforcement Learning
  2. Simulating random walks
  3. Optimal Portfolio Selection
  4. Forecasting stock market prices
  5. Delivery Vehicle Routing Application
  6. Prediction and Betting Evaluations of coin flips using Markov decision processes
  7. Build an optimized vending machine using Dynamic Programming
  8. Robot control system using Deep Reinforcement Learning
  9. Handwritten Digit Recognizer
  10. Playing the board game Go �
  11. What is next?

商品描述(中文翻譯)

《使用Keras掌握強化學習演算法的實用指南》

主要特點


  • 在機器人、遊戲和金融領域建立專案,實踐強化學習

  • 熟悉Keras並在真實世界的非結構化數據集上進行實踐

  • 探索蒙特卡羅、馬可夫決策和Q學習等高級深度學習演算法

書籍描述

強化學習在過去幾年中有了很大的發展,並證明在構建智能AI網絡方面是一種成功的技術。《Keras強化學習專案》使用強化學習的演算法和技術,結合更快的實驗庫Keras,將人類級別的性能應用於您的應用程序中。

本書首先介紹了使用Keras進行強化學習的概念。您將學習如何使用馬可夫鏈模擬隨機行走,並使用動態規劃(DP)和Python選擇最佳投資組合。您還將探索項目,例如使用蒙特卡羅方法預測股票價格,使用時間距離(TD)學習演算法提供車輛路線應用程序,以及使用馬可夫決策過程平衡旋轉機械系統。

一旦您瞭解了基礎知識,您將進一步模擬平衡車的建模,使用深度強化學習運行機器人控制系統,並使用圖像數據集在Python中構建手寫數字識別模型。最後,您將通過Q學習和強化學習演算法在圍棋遊戲中取得卓越成績。

通過閱讀本書,您不僅將對強化學習的概念、演算法和技術進行實踐培訓,還將準備好探索人工智能的世界。

您將學到什麼


  • 在預測和投注評估中實踐馬可夫決策過程

  • 實施蒙特卡羅方法預測環境行為

  • 探索TD學習演算法以管理倉庫操作

  • 使用Python和Keras構建深度Q網絡來控制機器人運動

  • 應用強化學習概念,使用圖像數據集構建手寫數字識別模型

  • 使用Q學習和OpenAI Gym解決博弈論問題

適合閱讀對象

《Keras強化學習專案》適合數據科學家、機器學習開發人員或人工智能工程師,他們希望通過開發實用專案來了解強化學習的基礎知識。對機器學習有扎實的知識和對Keras的基本熟悉將有助於您從本書中獲得最大的收益。

目錄


  1. Keras強化學習概述

  2. 模擬隨機行走

  3. 最佳投資組合選擇

  4. 預測股票市場價格

  5. 交付車輛路線應用程序

  6. 使用馬可夫決策過程預測和投注評估硬幣翻轉

  7. 使用動態規劃構建優化的自動販賣機

  8. 使用深度強化學習的機器人控制系統

  9. 手寫數字識別器

  10. 下棋遊戲

  11. 下一步是什麼?