TensorFlow 2 Reinforcement Learning Cookbook: Over 50 recipes to help you build, train, and deploy learning agents for real-world applications
暫譯: TensorFlow 2 強化學習食譜：超過 50 個食譜幫助您建立、訓練和部署實際應用的學習代理

Palanisamy, Praveen

TensorFlow 2 Reinforcement Learning Cookbook: Over 50 recipes to help you build, train, and deploy learning agents for real-world applications

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商品描述

Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning

Key Features

  • Develop and deploy deep reinforcement learning-based solutions to production pipelines, products, and services
  • Explore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic method
  • Customize and build RL-based applications for performing real-world tasks

Book Description

With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications.

Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you'll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x.

By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch.

What you will learn

  • Build deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras API
  • Implement state-of-the-art deep reinforcement learning algorithms using minimal code
  • Build, train, and package deep RL agents for cryptocurrency and stock trading
  • Deploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud services
  • Speed up agent development using distributed DNN model training
  • Explore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)

Who this book is for

The book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x.

商品描述(中文翻譯)

**發現使用強化學習開發 AI 應用程式以解決各種現實商業問題的配方**

#### 主要特點

- 開發和部署基於深度強化學習的解決方案到生產管道、產品和服務
- 探索流行的強化學習演算法，如 Q-learning、SARSA 和演員-評論者方法
- 自訂並構建基於強化學習的應用程式以執行現實任務

#### 書籍描述

透過深度強化學習，您可以構建智能代理、產品和服務，這些代理可以超越計算機視覺或感知來執行動作。TensorFlow 2.x 是用於開發和訓練深度神經網絡（DNN）的最受歡迎的深度學習框架的最新主要版本。本書包含易於遵循的配方，幫助您利用 TensorFlow 2.x 開發人工智慧應用程式。

本書從深度強化學習和 TensorFlow 2.x 的基本概念介紹開始，涵蓋 OpenAI Gym、基於模型的強化學習、無模型的強化學習，以及如何開發基本代理。您將學習如何實現先進的深度強化學習演算法，如演員-評論者、深度確定性策略梯度、深度 Q 網絡、近端策略優化和深度遞迴 Q 網絡，以訓練您的強化學習代理。隨著進展，您將通過構建加密貨幣交易代理、股票/股份交易代理和自動化任務完成的智能代理來探索強化學習的應用。最後，您將了解如何將深度強化學習代理部署到雲端，並使用 TensorFlow 2.x 構建跨平台應用程式。

在本書結束時，您將對深度強化學習演算法及其從零開始的實現有扎實的理解。

#### 您將學到什麼

- 使用全新的 TensorFlow 2.x 和 Keras API 從零開始構建深度強化學習代理
- 使用最少的代碼實現最先進的深度強化學習演算法
- 構建、訓練和打包用於加密貨幣和股票交易的深度強化學習代理
- 將強化學習代理部署到雲端和邊緣，通過創建桌面、網頁和移動應用程式及雲服務來測試它們
- 使用分散式 DNN 模型訓練加速代理開發
- 探索分散式深度強化學習架構，並發現 AIaaS（人工智慧即服務）的機會

#### 本書適合誰

本書適合機器學習應用程式開發者、AI 和應用 AI 研究人員、數據科學家、深度學習實踐者，以及對強化學習概念有基本了解的學生，這些讀者希望使用 TensorFlow 2.x 從零開始構建、訓練和部署自己的強化學習系統。

作者簡介

Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.

作者簡介(中文翻譯)

Praveen Palanisamy 目前專注於開發自主智能系統。他目前是通用汽車（General Motors）研究與開發部門的人工智慧研究員。他開發使用深度強化學習的規劃和決策演算法及系統，以應用於自主駕駛。之前，他在卡內基梅隆大學（Carnegie Mellon University）的機器人研究所工作，專注於自主導航，包括移動機器人的感知和人工智慧。他擁有從零開始開發完整自主機器人系統的經驗。

目錄大綱

  1. Developing building blocks for Deep RL using TensorFlow 2.x
  2. Implementing value-based, policy gradients and actor-critic Deep RL algorithms
  3. Implementing Advanced Deep RL algorithms
  4. RL in real-world: Building intelligent trading agents
  5. RL in Real-World: Building Stock Trading Agents
  6. RL in real-world: Building intelligent agents to complete your ToDos
  7. Deploying Deep RL Agents to the Cloud
  8. Building cross-platform (web, desktop, mobile) Deep-RL Apps using TensorFlow 2.x
  9. Distributed training and automated production deployment pipeline for Deep RL Apps

目錄大綱(中文翻譯)


  1. Developing building blocks for Deep RL using TensorFlow 2.x

  2. Implementing value-based, policy gradients and actor-critic Deep RL algorithms

  3. Implementing Advanced Deep RL algorithms

  4. RL in real-world: Building intelligent trading agents

  5. RL in Real-World: Building Stock Trading Agents

  6. RL in real-world: Building intelligent agents to complete your ToDos

  7. Deploying Deep RL Agents to the Cloud

  8. Building cross-platform (web, desktop, mobile) Deep-RL Apps using TensorFlow 2.x

  9. Distributed training and automated production deployment pipeline for Deep RL Apps

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