M. Lapan. Packt Publishing, Birmingham, UK, (2018)
Abstract
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. This 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. 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.
%0 Book
%1 Lapan18
%A Lapan, Maxim
%C Birmingham, UK
%D 2018
%I Packt Publishing
%K 02041 103 8book 9safari ai development learn numerical software
%T Deep Reinforcement Learning Hands-On
%X 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. This 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. 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.
%@ 978-1-78883-424-7
@book{Lapan18,
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abstract = {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. This 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. 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.},
added-at = {2019-03-23T19:47:18.000+0100},
address = {Birmingham, UK},
author = {Lapan, Maxim},
biburl = {https://www.bibsonomy.org/bibtex/26e5cbaf5acfed86f2f1e623be404f110/flint63},
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isbn = {978-1-78883-424-7},
keywords = {02041 103 8book 9safari ai development learn numerical software},
owner = {flint},
pagetotal = {546},
publisher = {Packt Publishing},
referencetype = {book},
subtitle = {Apply Modern RL Methods, with Deep Q-Networks, Value Iteration, Policy Gradients, TRPO, AlphaGo Zero and more},
timestamp = {2019-03-23T19:47:18.000+0100},
title = {Deep Reinforcement Learning Hands-On},
x.asin = {1788834240},
x.sortdate = {2018-06},
year = 2018
}