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Deep Reinforcement Learning Hands-On
Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF , Third Edition

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Profile Icon Maxim Lapan
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R$80 R$258.99
eBook Nov 2024 716 pages 3rd Edition
eBook
R$80 R$258.99
Paperback
R$323.99
Subscription
Free Trial
Renews at R$50p/m
Arrow left icon
Profile Icon Maxim Lapan
Arrow right icon
R$80 R$258.99
eBook Nov 2024 716 pages 3rd Edition
eBook
R$80 R$258.99
Paperback
R$323.99
Subscription
Free Trial
Renews at R$50p/m
eBook
R$80 R$258.99
Paperback
R$323.99
Subscription
Free Trial
Renews at R$50p/m

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Key benefits

  • Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation
  • Develop deep RL models, improve their stability, and efficiently solve complex environments
  • New content on RL from human feedback (RLHF), MuZero, and transformers

Description

Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the fi eld, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers. The book retains its approach of providing concise and easy-to-follow explanations from the previous editions. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and its use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion

Who is this book for?

This book is ideal for machine learning engineers, software engineers, and data scientists looking to learn and apply deep reinforcement learning in practice. It assumes familiarity with Python, calculus, and machine learning concepts. With practical examples and high-level overviews, it’s also suitable for experienced professionals looking to deepen their understanding of advanced deep RL methods and apply them across industries, such as gaming and finance

What you will learn

  • Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs
  • Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG
  • Implement RL algorithms using PyTorch and modern RL libraries
  • Build and train deep Q-networks to solve complex tasks in Atari environments
  • Speed up RL models using algorithmic and engineering approaches
  • Leverage advanced techniques like proximal policy optimization (PPO) for more stable training

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 12, 2024
Length: 716 pages
Edition : 3rd
Language : English
ISBN-13 : 9781835882719
Vendor :
Facebook , OpenAI
Category :
Languages :
Tools :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Nov 12, 2024
Length: 716 pages
Edition : 3rd
Language : English
ISBN-13 : 9781835882719
Vendor :
Facebook , OpenAI
Category :
Languages :
Tools :

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