Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow (Paperback)

Sean Saito, Yang Wenzhuo, Rajalingappaa Shanmugamani



Deploy autonomous agents in business systems using powerful Python libraries and sophisticated reinforcement learning models

Key Features

  • Implement Q-learning and Markov models with Python and OpenAI
  • Explore the power of TensorFlow to build self-learning models
  • Eight AI projects to gain confidence in building self-trained applications

Book Description

Reinforcement learning (RL) is the next big leap in the artificial intelligence domain, given that it is unsupervised, optimized, and fast. Python Reinforcement Learning Projects takes you through various aspects and methodologies of reinforcement learning, with the help of insightful projects.

You will learn about core concepts of reinforcement learning, such as Q-learning, Markov models, the Monte-Carlo process, and deep reinforcement learning. As you make your way through the book, you'll work on projects with various datasets, including numerical, text, video, and audio, and will gain experience in gaming, image rocessing, audio processing, and recommendation system projects. You'll explore TensorFlow and OpenAI Gym to implement a deep learning RL agent that can play an Atari game. In addition to this, you will learn how to tune and configure RL algorithms and parameters by building agents for different kinds of games. In the concluding chapters, you'll get to grips with building self-learning models that will not only uncover layers of data but also reason and make decisions.

By the end of this book, you will have created eight real-world projects that explore reinforcement learning and will have handson experience with real data and artificial intelligence (AI) problems.

What you will learn

  • Train and evaluate neural networks built using TensorFlow for RL
  • Use RL algorithms in Python and TensorFlow to solve CartPole balancing
  • Create deep reinforcement learning algorithms to play Atari games
  • Deploy RL algorithms using OpenAI Universe
  • Develop an agent to chat with humans
  • Implement basic actor-critic algorithms for continuous control
  • Apply advanced deep RL algorithms to games such as Minecraft
  • Autogenerate an image classifier using RL

Who this book is for

Python Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. Individuals who want to work on self-learning model projects will also find this book useful.

Table of Contents

  1. Up and running with Reinforcement Learning
  2. Balancing Cart Pole
  3. Playing ATARI Games
  4. Simulating Control Tasks
  5. Building Virtual Worlds in Minecraft
  6. Learning to Play Go
  7. Creating a Chatbot
  8. Generating a Deep Learning Image Classifier
  9. Predicting Future Stock Prices
  10. Looking Ahead




- 使用Python和OpenAI實現Q學習和馬爾可夫模型。
- 探索TensorFlow的威力,建立自學習模型。
- 八個人工智慧專案,讓您在建立自訓練應用程式方面更有信心。



您將學習強化學習的核心概念,如Q學習、馬爾可夫模型、蒙特卡羅過程和深度強化學習。隨著您閱讀本書,您將使用各種數據集進行專案,包括數值、文本、視頻和音頻,並在遊戲、圖像處理、音頻處理和推薦系統專案中獲得經驗。您將探索TensorFlow和OpenAI Gym,實現一個可以玩Atari遊戲的深度學習強化學習代理。此外,您還將通過為不同類型的遊戲建立代理,學習如何調整和配置強化學習算法和參數。在最後幾章中,您將掌握建立自學習模型的技巧,這些模型不僅能揭示數據的層次,還能進行推理和做出決策。



- 使用TensorFlow訓練和評估強化學習的神經網絡。
- 使用Python和TensorFlow的強化學習算法解決CartPole平衡問題。
- 創建深度強化學習算法來玩Atari遊戲。
- 使用OpenAI Universe部署強化學習算法。
- 開發一個與人類對話的代理。
- 實現基本的演員-評論家算法進行連續控制。
- 將先進的深度強化學習算法應用於Minecraft等遊戲。
- 使用強化學習自動生成圖像分類器。



1. 強化學習入門
2. 平衡Cart Pole
3. 玩Atari遊戲
4. 模擬控制任務
5. 在Minecraft中建立虛擬世界
6. 學習下棋
7. 創建聊天機器人
8. 生成深度學習圖像分類器
9. 預測未來股價
10. 展望未來