Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
Sudharsan Ravichandiran
- 出版商: Packt Publishing
- 出版日期: 2018-06-28
- 定價: $1,480
- 售價: 9.0 折 $1,332
- 語言: 英文
- 頁數: 318
- 裝訂: Paperback
- ISBN: 1788836529
- ISBN-13: 9781788836524
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相關分類:
Python、程式語言、Reinforcement、DeepLearning、TensorFlow
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相關翻譯:
用 Python 實作強化學習|使用 TensorFlow 與 OpenAI Gym (Hands-On Reinforcement Learning with Python) (繁中版)
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其他版本:
Deep Reinforcement Learning with Python, 2/e (Paperback)
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相關主題
商品描述
A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python
Key Features
- Your entry point into the world of artificial intelligence using the power of Python
- An example-rich guide to master various RL and DRL algorithms
- Explore various state-of-the-art architectures along with math
Book Description
Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.
The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.
By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
What you will learn
- Understand the basics of reinforcement learning methods, algorithms, and elements
- Train an agent to walk using OpenAI Gym and Tensorflow
- Understand the Markov Decision Process, Bellman's optimality, and TD learning
- Solve multi-armed-bandit problems using various algorithms
- Master deep learning algorithms, such as RNN, LSTM, and CNN with applications
- Build intelligent agents using the DRQN algorithm to play the Doom game
- Teach agents to play the Lunar Lander game using DDPG
- Train an agent to win a car racing game using dueling DQN
Who This Book Is For
If you're a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.
Table of Contents
- Introduction to Reinforcement Learning
- Getting started with OpenAI and Tensorflow
- Markov Decision process and Dynamic Programming
- Gaming with Monte Carlo Tree Search
- Temporal Difference Learning
- Multi-Armed Bandit Problem
- Deep Learning Fundamentals
- Deep Learning and Reinforcement
- Playing Doom With Deep Recurrent Q Network
- Asynchronous Advantage Actor Critic Network
- Policy Gradients and Optimization
- Capstone Project Car Racing using DQN
- Current Research and Next Steps
商品描述(中文翻譯)
一本以Python為基礎的實踐指南,充滿例子,幫助您掌握深度強化學習算法。
主要特點:
- 使用Python的人工智慧世界的入門點
- 以例子豐富的指南,掌握各種強化學習和深度強化學習算法
- 探索各種最先進的架構和數學概念
書籍描述:
強化學習(RL)是人工智慧中最熱門且最有前景的分支。《使用Python進行實踐強化學習》將幫助您掌握基本的強化學習算法,還有進階的深度強化學習算法。
本書首先介紹強化學習,接著介紹OpenAI Gym和TensorFlow。然後,您將探索各種強化學習算法和概念,例如馬可夫決策過程、蒙特卡羅方法和動態規劃,包括值迭代和策略迭代。這本充滿例子的指南還將介紹您深度強化學習算法,例如Dueling DQN、DRQN、A3C、PPO和TRPO。您還將學習關於增強學習的最新進展,例如增強想像代理、從人類偏好中學習、DQfD、HER等等。
通過閱讀本書,您將獲得實施強化學習和深度強化學習的所有知識和經驗,並準備好進入人工智慧的世界。
您將學到:
- 理解強化學習方法、算法和元素的基礎知識
- 使用OpenAI Gym和Tensorflow訓練一個走路的智能體
- 理解馬可夫決策過程、貝爾曼最優性和TD學習
- 使用各種算法解決多臂搶劫問題
- 掌握深度學習算法,例如RNN、LSTM和CNN,並應用於實際場景
- 使用DRQN算法建立智能體來玩Doom遊戲
- 使用DDPG訓練智能體來玩Lunar Lander遊戲
- 使用Dueling DQN訓練智能體來贏得賽車遊戲
本書適合對人工智慧感興趣的機器學習開發人員或深度學習愛好者,並且想從頭開始學習強化學習。具備線性代數、微積分和Python編程語言的知識將有助於理解本書中涵蓋的概念。
目錄:
1. 強化學習簡介
2. 開始使用OpenAI和Tensorflow
3. 馬可夫決策過程和動態規劃
4. 使用蒙特卡羅樹搜索進行遊戲
5. 時間差分學習
6. 多臂搶劫問題
7. 深度學習基礎知識
8. 深度學習和強化學習
9. 使用深度循環Q網絡玩Doom
10. 非同步優勢演員評論網絡
11. 策略梯度和優化
12. 毕业项目:使用DQN玩賽車遊戲
13. 當前研究和下一步計劃