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

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.

發現使用強化學習開發 AI 應用程式，以解決各種真實商業問題的食譜

主要特點

• 開發並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算法，如 Q-learning、SARSA 和演員評論法


• 自定義並構建基於強化學習的應用程式，執行真實世界任務

書籍描述

使用深度強化學習，您可以建立智能代理、產品和服務，超越電腦視覺或感知，執行動作。TensorFlow 2.x 是最受歡迎的深度學習框架的最新主要版本，用於開發和訓練深度神經網絡（DNNs）。本書包含使用 TensorFlow 2.x 開發人工智能應用程式的易於遵循的食譜。

從深度強化學習和 TensorFlow 2.x 的基礎入門開始，本書涵蓋了 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。您將發現如何實現高級深度強化學習算法，如演員評論法、深度確定性策略梯度、深度 Q 網絡、近端策略優化和深度遞歸 Q 網絡，以訓練您的強化學習代理。隨著進一步的學習，您將通過構建加密貨幣交易代理、股票交易代理和智能代理來探索強化學習的應用，以自動完成任務。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。

通過閱讀本 TensorFlow 書籍，您將從頭開始獲得對深度強化學習算法及其實現的扎實理解。

您將學到什麼

• 使用全新的 TensorFlow 2.x 和 Keras API 從頭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算法


• 為加密貨幣和股票交易構建、訓練和打包深度強化學習代理


• 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以及雲服務來測試它們


• 使用分佈式 DNN 模型訓練加速代理開發


• 探索分佈式深度強化學習架構，並發現 AIaaS（AI 作為服務）的機會

本書適合對象

本書適合機器學習應用程式開發人員、AI 和應用 AI 研究人員、數據科學家、深度學習從業人員以及具備基本強化學習概念的學生，他們希望使用 TensorFlow 2.x 從頭開始構建、訓練和部署自己的強化學習系統。

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.

Praveen Palanisamy 是一位致力於開發自主智能系統的人。他目前在通用汽車研發部擔任人工智能研究員。他開發了使用深度強化學習的規劃和決策算法以及自主駕駛系統。在此之前，他在卡內基梅隆大學的機器人研究所工作，研究自主導航，包括移動機器人的感知和人工智能。他有從頭開發完整自主機器人系統的經驗。

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

- 使用 TensorFlow 2.x 開發深度強化學習的基礎模塊
- 實現基於價值、策略梯度和演員評論者的深度強化學習算法
- 實現高級深度強化學習算法
- 在現實世界中應用強化學習：建立智能交易代理
- 在現實世界中應用強化學習：建立股票交易代理
- 在現實世界中應用強化學習：建立智能代理以完成待辦事項
- 部署深度強化學習代理到雲端
- 使用 TensorFlow 2.x 建立跨平台（網頁、桌面、手機）的深度強化學習應用程式
- 深度強化學習應用程式的分散訓練和自動化生產部署流程