Description: Further DetailsTitle: Deep Reinforcement Learning Hands-OnCondition: NewSubtitle: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and moreEAN: 9781788834247ISBN: 9781788834247Publisher: Packt Publishing LimitedFormat: PaperbackRelease Date: 06/21/2018Description: 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 chatbotsBook Description Recent 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 chatbotsWho this book is for Some 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.Language: EnglishCountry/Region of Manufacture: GBItem Height: 93mmItem Length: 75mmAuthor: Maxim LapanGenre: Computing & InternetISBN-10: 1788834240Release Year: 2018 Missing Information?Please contact us if any details are missing and where possible we will add the information to our listing.
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Book Title: Deep Reinforcement Learning Hands-On
Title: Deep Reinforcement Learning Hands-On
Subtitle: Apply modern RL methods, with deep Q-networks, value iteration, p
EAN: 9781788834247
ISBN: 9781788834247
Release Date: 06/21/2018
Release Year: 2018
Country/Region of Manufacture: GB
Item Height: 93mm
Genre: Computing & Internet
ISBN-10: 1788834240
Number of Pages: 546 Pages
Publication Name: Deep Reinforcement Learning Hands-On : Apply Modern RL Methods, with Deep Q-Networks, Value Iteration, Policy Gradients, TRPO, AlphaGo Zero and More
Language: English
Publisher: Packt Publishing, The Limited
Publication Year: 2018
Subject: Programming / Algorithms, Intelligence (Ai) & Semantics, Natural Language Processing
Type: Textbook
Subject Area: Computers
Author: Maxim Lapan
Item Length: 3.6 in
Item Width: 3 in
Format: Trade Paperback