CB-India Bonus: Surprise for Frequent Buyers, add books to the cart now!!  
SAP Press, Shroff Publishing and books worth Rs.2000 & above, no shipping charges!!  
Now Pay on Delivery also Available!!
Kindly Note due to the on-going Pandemic Crisis, there can be unexpected delays in 
delivering/procurement of books, we will try our best to supply the books in the 
time frame mentioned but there can be delays beyond our control. 
Please bear with us.

0 Your Cart

Python Reinforcement Learning:

Solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow

Author: Sudharsan Ravichandiran

Sudharsan Ravichandiran (Author)
Visit Cb-India's Author Page
Books by him and info about author and more.

Are you a Author?
Learn more here

Save
83%

Hover over an image to enlarge

MRP: MRP: $44.43
Net Price: $7.83
You save: $36.60 (82.38%)
Leadtime to ship in days (default): E-Book Immediate, Print Book usually ships in 7-8 days

This product is electronically distributed.

9781838649777
Price in points: 599 points
Reward points: 6 points

Minimum quantity for "Python Reinforcement Learning:

Solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow

" is 1.

Please sign in to buy

This product cannot be added to the
cart because you are not logged in.

Add to wish list Compare

Share

Description

Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries

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 the power of modern Python libraries to gain confidence in building self-trained applications

Book Description

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.

The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL.

By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems.

This Learning Path includes content from the following Packt products:

  • Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran
  • Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani

What you will learn

  • Train an agent to walk using OpenAI Gym and TensorFlow
  • Solve multi-armed-bandit problems using various algorithms
  • Build intelligent agents using the DRQN algorithm to play the Doom game
  • Teach your agent to play Connect4 using AlphaGo Zero
  • Defeat Atari arcade games using the value iteration method
  • Discover how to deal with discrete and continuous action spaces in various environments

Who this book is for

If you're an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.

Table of Contents

  1. Introduction to Reinforcement Learning
  2. Getting Started with OpenAI and TensorFlow
  3. The Markov Decision Process and Dynamic Programming
  4. Gaming with Monte Carlo Methods
  5. Temporal Difference Learning
  6. Multi-Armed Bandit Problem
  7. Playing Atari Games
  8. Atari Games with Deep Q Network
  9. Playing Doom with a Deep Recurrent Q Network
  10. The Asynchronous Advantage Actor Critic Network
  11. Policy Gradients and Optimization
  12. Balancing CartPole
  13. Simulating Control Tasks
  14. Building Virtual Worlds in Minecraft
  15. Learning to Play Go
  16. Creating a Chatbot
  17. Generating a Deep Learning Image Classifier
  18. Predicting Future Stock Prices
  19. Capstone Project - Car Racing Using DQN
  20. Looking Ahead

Features

Author:
Sudharsan Ravichandiran
Binding:
E-Book
Country Origin:
UK
Edition :
1
Leadtime to ship in days (default):
E-Book Immediate, Print Book usually ships in 7-8 days
Page:
496
Publisher:
Packt Publishing (E-Books)
Year:
2019

Tags

Reviews

No posts found

Possibly you may be interested
 
Fast and high quality delivery

Our company makes delivery all over the country

Quality assurance and service

We offer only those goods, in which quality we are sure