Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLH - Marjane Mall - Image 1
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Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLH

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Livraison entre le Jeudi 10 septembre 2026 et le Vendredi 11 septembre 2026

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Marque : GENERIC
Vendu par KECHBOOK

Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to modern, state-of-the-art methodsPurchase of the print or Kindle book includes a free PDF eBookFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key...

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Description produit

Marque
GENERIC
Titre
Deep Reinforcement Learning Hands-On: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF
Éditeur
Packt Publishing
Type de produit
paperback
Présentation du livre
paperback
Date de sortie
11/12/2024 12:00:00 AM
Langue d'origine
English
ISBN
1835882706
Nombre de pages
716 pages
Langue
English
Résumé
Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to modern, state-of-the-art methodsPurchase of the print or Kindle book includes a free PDF eBookFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesLearn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigationDevelop deep RL models, improve their stability, and efficiently solve complex environmentsNew content on RL from human feedback (RLHF), MuZero, and transformersBook DescriptionStart 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 field, 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*Email sign-up and proof of purchase requiredWhat you will learnStay o
Auteur
Maxim Lapan
Date de parution
11/12/2024 12:00:00 AM