Rated 4.2/5 based on 102
Awesome Book - by , @book.updated_at
5/ 5stars
This is an awesome book, we should definitely buy it.
Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Book Specification

Binding Paperback
Language English
Number Of Pages 546
Author Maxim Lapan
Publisher Packt Publishing Limited
Isbn-10 1788834240
Isbn-13 9781788834247
Dimension 19.05*3.12*23.5

Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Maxim Lapan's Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest Algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic Algorithms Keep up with the very latest industry developments, including AI-driven chatbots Book DescriptionRecent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google's use of Algorithms to play and defeat the well-known Atari arcade Games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL Tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world Environments. Take on both the Atari set of virtual Games and Family favorites such as Connect4. The book provides an introduction to the Basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on `grid world' Environments, teach your agent to buy and trade stocks, and find out how natural language Models are driving the boom in chatbots. What you will learn Understand the DL context of RL and implement complex DL Models Learn the Foundation of RL: Markov decision processes Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others Discover how to deal with discrete and continuous action spaces in various Environments Defeat Atari arcade Games using the value iteration method Create your own OpenAI Gym Environment to train a stock trading agent Teach your agent to play Connect4 using AlphaGo Zero Explore the very latest deep RL Research on topics including AI-driven chatbots Who this book is forSome fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to Readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.
Store Price Buy Now
Amazon, Paperback Rs. 1499.0

Why you should read Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more by Maxim Lapan

This book has been written by Maxim Lapan, who has written books like Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition,Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. The books are written in Algorithms,Computer Science category. This book is read by people who are interested in reading books in category : Computer Science. So, if you want to explore books similar to This book, you must read and buy this book.

Other books by same author

Book Binding Language
Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition Paperback English
Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more Paperback English

How long would it take for you to read Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Depending on your reading style, this is how much time you would take to complete reading this book.

Reading Style Time To Finish The Book
Slow 109 hours
Average 54 hours
Good 36 hours
Excellent 18 hours
So if you are a Reader belonging in the Good category, and you read it daily for 1 hour, it will take you 36 days.
Note: A slow reader usually reads 100 words per minute, an average reader 200 words per minute, an average reader 300 words per minute and an excellent leader reads about 600-1000 words per minute, however the comprehension may vary.

Searches in World for Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

City Country Count
46
Top Read Books

Top Reads