Packt.Hands-On.Reinforcement.Learning.with.Python.1788836529

相似文件 换一批

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

Packt.Hands-On.Reinforcement.Learning.with.Python.1788836529(true pdf)

Hands-On Reinforcement Learning with Python: Master reinforcement learning and deep reinforcement learning by building intelligent applications using OpenAI, TensorFlow, and Python

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

Chapter 1. Introduction to Reinforcement Learning
Chapter 2. Getting started with OpenAI and Tensorflow
Chapter 3. Markov Decision process and Dynamic Programming
Chapter 4. Gaming with Monte Carlo Tree Search
Chapter 5. Temporal Difference Learning
Chapter 6. Multi-Armed Bandit Problem
Chapter 7. Deep Learning Fundamentals
Chapter 8. Deep Learning and Reinforcement
Chapter 9. Playing Doom With Deep Recurrent Q Network
Chapter 10. Asynchronous Advantage Actor Critic Network
Chapter 11. Policy Gradients and Optimization
Chapter 12. Capstone Project – Car Racing using DQN
Chapter 13. Current Research and Next Step

Packt.Hands-On.Reinforcement.Learning.with.Python.1788836529.rar (14.2 MB, 需要: 2 个论坛币) 本附件包括：

• Packt.Hands-On.Reinforcement.Learning.with.Python.1788836529.pdf

2019-9-3 22:05:31 上传

Packt.Hands-On.Reinforcement.Learning.with.Python.1788836529
需要: 2 个论坛币  [购买]

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖