Deep Reinforcement Learning Hands-On
Lapan, Maxim
Sold by GreatBookPrices, Columbia, MD, U.S.A.
AbeBooks Seller since April 6, 2009
Used - Soft cover
Condition: Used - As new
Quantity: 6 available
Add to basketSold by GreatBookPrices, Columbia, MD, U.S.A.
AbeBooks Seller since April 6, 2009
Condition: Used - As new
Quantity: 6 available
Add to basketUnread book in perfect condition.
Seller Inventory # 33186436
Publisher's Note: This edition from 2018 is outdated and not compatible with any of the most recent updates to Python libraries. A new third edition, updated for 2020 with six new chapters that include multi-agent methods, discrete optimization, RL in robotics, and advanced exploration techniques is now available.
This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.
Key Features
Book Description
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
Who 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.
"About this title" may belong to another edition of this title.
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