Reinforcement Learning with Tensorflow: Learn the art of designing self-learning systems with TensorFlow and OpenAI Gym

Sayon Dutta

  • 出版商: Packt Publishing
  • 出版日期: 2018-04-24
  • 售價: $2,110
  • 貴賓價: 9.5$2,005
  • 語言: 英文
  • 頁數: 334
  • 裝訂: Paperback
  • ISBN: 1788835727
  • ISBN-13: 9781788835725
  • 相關分類: ReinforcementDeepLearningTensorFlow
  • 海外代購書籍(需單獨結帳)
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商品描述

A Detailed Step-by-Step Guide covering Reinforcement Learning concepts, techniques and various frameworks to develop self learning systems

Key Features

  • Become familiar with reinforcement learning concepts and learn how to implement them using TensorFlow
  • Implement different problem-solving methods for Reinforcement Learning such as dynamic programming, Monte Carlo methods, and more
  • Explore various reinforcement earning use-cases such as autonomous driving cars, robobrokers, and learning robots

Book Description

Reinforcement Learning (RL) is the next emerging area in the space of Artificial Intelligence and allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence-from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions.

The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it's gaining so much popularity. Furthermore, it show readers how to put the concepts to practical use with the help of TensorFlow and OpenAI Gym to train efficient deep reinforcement learning neural networks. The book also discusses reinforcement learning and the rewarding system: Markov Decision Processes (MDPs), Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learnings such as Q-learning and SARSA-

We see how reinforcement learning algorithms play a role in image processing and NLP, and how they can be used with TensorFlow and OpenAI Gym to build simple neural network models.

By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym.

What you will learn

  • Explore the applications of reinforcement learning in advertisement, image processing, and NLP
  • Master various aspects of RL such as Deep-Q-Network, A3C, Q Learning, and more
  • How Reinforcement Learning can be applied to robotics, autonomous vehicles, and finance.
  • Frameworks and technologies to implement the various RL mechanisms
  • Implement state-of-the-art RL algorithms from the basics
  • Build pipelines, systems, and applications using RL techniques
  • Teach an RL network to play a game using TensorFlow and/or the OpenAI gym framework
  • Develop new systems that can learn, understand the environment, and make decisions

Who This Book Is For

If you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required

商品描述(中文翻譯)

一本詳細的逐步指南,涵蓋了強化學習的概念、技術和各種框架,以開發自主學習系統。

主要特點:
- 熟悉強化學習概念,並學習如何使用TensorFlow實現它們。
- 實現強化學習的不同問題解決方法,如動態規劃、蒙特卡羅方法等。
- 探索各種強化學習應用案例,如自動駕駛汽車、機器人經紀人和學習機器人。

書籍描述:
強化學習(RL)是人工智能領域中的下一個新興領域,它允許您在業務環境中開發智能、快速和自主學習的系統。它是一種有效的方法,用於訓練學習代理人並解決各種人工智能問題,從遊戲、自駕車和機器人到企業應用,範圍從數據中心節能(冷卻數據中心)到智能倉儲解決方案。

本書介紹了深度強化學習在深度神經網絡架構與強化學習的協同作用下取得的重大進展和成功。本書還向讀者介紹了強化學習的概念、優勢以及為什麼它如此受歡迎。此外,它還展示了如何通過TensorFlow和OpenAI Gym將這些概念實際應用於實踐中,以訓練高效的深度強化學習神經網絡。本書還討論了強化學習和獎勵系統:馬爾可夫決策過程(MDPs)、蒙特卡羅樹搜索、動態規劃(如策略和價值迭代)、時間差分學習(如Q學習和SARSA)。

我們將看到強化學習算法在圖像處理和自然語言處理中的作用,以及如何使用TensorFlow和OpenAI Gym構建簡單的神經網絡模型。

通過閱讀本書,您將對強化學習有深入的理解,並能夠利用TensorFlow和OpenAI Gym的強大功能將您的知識實際應用。

您將學到:
- 探索強化學習在廣告、圖像處理和自然語言處理中的應用。
- 掌握深度Q網絡、A3C、Q學習等強化學習的各個方面。
- 如何將強化學習應用於機器人、自動駕駛車輛和金融領域。
- 實現各種強化學習機制的框架和技術。
- 從基礎開始實現最先進的強化學習算法。
- 使用強化學習技術構建流程、系統和應用。
- 使用TensorFlow和/或OpenAI Gym框架教授強化學習網絡玩遊戲。
- 開發能夠學習、理解環境並做出決策的新系統。

本書適合對使用TensorFlow進行強化學習的實際應用感興趣的讀者。本書假設讀者具有機器學習和神經網絡編程概念的先備知識,以及對TensorFlow框架的一些了解。不需要先前的強化學習經驗。