Deep Reinforcement Learning Hands-On. (Record no. 16036)
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| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250710182907.0 |
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| fixed length control field | 250616s||||||||||||||||o||||||||||| |d |
| 024 80 - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 9781835882719 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PACKT |
| Transcribing agency | PACKT |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 044 ## - COUNTRY OF PUBLISHING/PRODUCING ENTITY CODE | |
| MARC country code | GB |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Maxim Lapan |
| Relator term | author. |
| 245 00 - TITLE STATEMENT | |
| Title | Deep Reinforcement Learning Hands-On. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | GB: |
| Name of publisher, distributor, etc. | Packt, |
| Date of publication, distribution, etc. | 2024-11-12. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2024-11-12 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 716. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <p><b>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 methods Purchase of the print or Kindle book includes a free PDF eBook</b></p><h4>Key Features</h4><ul><li>Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation</li><li>Develop deep RL models, improve their stability, and efficiently solve complex environments</li><li>New content on RL from human feedback (RLHF), MuZero, and transformers</li></ul><h4>Book Description</h4>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<h4>What you will learn</h4><ul><li>Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs</li><li>Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG</li><li>Implement RL algorithms using PyTorch and modern RL libraries</li><li>Build and train deep Q-networks to solve complex tasks in Atari environments</li><li>Speed up RL models using algorithmic and engineering approaches</li><li>Leverage advanced techniques like proximal policy optimization (PPO) for more stable training</li></ul><h4>Who this book is for</h4>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. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Data in extended ASCII character set. |
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| System details note | Mode of access: Internet. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PACKT |
| 773 0# - HOST ITEM ENTRY | |
| Title | Deep Reinforcement Learning Hands-On |
| Place, publisher, and date of publication | GB,Packt,2024-11-12 |
| Physical description | 716 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/482392">https://learning.packt.com/product/482392</a> |
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