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

1. What is next?

2. 
   
3. 主要特点
4. 
   
5. 在机器人、游戏和金融领域建立项目，将强化学习（RL）付诸实施
6. 掌握Keras并在真实世界的非结构化数据集上进行练习
7. 
   
8. 揭示先进的深度学习算法，如蒙特卡洛、马尔科夫决策和Q-learning。
9. 
   
10. 书中描述
11. 强化学习在过去几年里有了很大的发展，并被证明是构建智能和智慧的人工智能网络的成功技术。Keras强化学习项目使用强化学习的算法和技术，加上Keras这个更快的实验库，将人类水平的性能安装到你的应用程序中。
12. 
    
13. 本书首先让你了解使用Keras进行强化学习的概念。你将学习如何使用马尔科夫链模拟随机行走，并使用动态编程（DP）和Python选择最佳组合。你还将探索一些项目，如使用蒙特卡洛方法预测股票价格，使用时空距离（TD）学习算法提供车辆路由应用，以及使用马尔科夫决策过程平衡一个旋转机械系统。
14. 
    
15. 一旦你理解了基础知识，你将继续对赛格威进行建模，使用深度强化学习运行一个机器人控制系统，并使用图像数据集在Python中建立一个手写数字识别模型。最后，你将在Q-Learning和强化学习算法的帮助下，在玩棋盘游戏方面表现出色。
16. 
    
17. 在本书结束时，你不仅会在强化学习的概念、算法和技术方面得到实战训练，而且还能为探索人工智能的世界做好准备。
18. 
    
19. 你将学到什么
20. 
    
21. 在预测和投注评估中实践马尔科夫决策过程
22. 
    
23. 实施蒙特卡洛方法来预测环境行为
24. 
    
25. 探索TD学习算法来管理仓库运作
26. 
    
27. 使用Python和Keras构建一个深度Q网络来控制机器人的运动
28. 
    
29. 应用强化概念，利用图像数据集建立手写数字识别模型
30. 
    
31. 使用Q-Learning和OpenAI Gym解决博弈论问题
32. 
    
33. 
    
34. 本书适用对象
35. 如果你是数据科学家、机器学习开发者或人工智能工程师，想通过开发实际项目来了解强化学习的基本原理，那么Keras强化学习项目就适合你。完善的机器学习知识和对Keras的基本熟悉，对获得本书的大部分内容是有用的。
36. 
    
37. 
    
38. 目录
39. 
    
40. Keras强化学习概述
41. 
    
42. 模拟随机游走
43. 
    
44. 最佳投资组合选择
45. 
    
46. 预测股票市场价格
47. 
    
48. 送货车辆路由应用
49. 
    
50. 预测和投注 使用马尔可夫决策过程对抛硬币进行评估
51. 
    
52. 利用动态编程构建优化的自动售货机
53. 
    
54. 使用深度强化学习的机器人控制系统
55. 
    
56. 手写数字识别器
57. 
    
58. 下围棋棋盘游戏
59. 
    
60. 下一步是什么？