MARC details
| 000 -LEADER |
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| fixed length control field |
04408nam a22003017a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260520150758.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
230801b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| ISBN |
9781835882702 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| ISBN |
1835882706 |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 LAP-D |
| 100 ## - MAIN ENTRY--AUTHOR NAME |
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| Personal name |
Lapan, Maxim |
| 245 ## - TITLE STATEMENT |
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| Title |
Deep reinforcement learning hands-on. |
| Remainder of title |
A practical and easy-to-follow guide to RL from Q-Learning and DQNs to PPO and RLHF |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
3rd |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication |
Brimingham, UK |
| Name of publisher |
Packt Publishing |
| Year of publication |
2024 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Number of Pages |
xxviii, 684p. |
| 500 ## - GENERAL NOTE |
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| General note |
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<br/><br/>Purchase of the print or Kindle book includes a free PDF eBook<br/><br/>Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*<br/><br/>Key Features:<br/><br/>- Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation<br/><br/>- Develop deep RL models, improve their stability, and efficiently solve complex environments<br/><br/>- New content on RL from human feedback (RLHF), MuZero, and transformers |
| 500 ## - GENERAL NOTE |
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| General note |
Who this book is for:<br/>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 |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Table of Contents<br/><br/>- What Is Reinforcement Learning?<br/><br/>- OpenAI Gym<br/><br/>- Deep Learning with PyTorch<br/><br/>- The Cross-Entropy Method<br/><br/>- Tabular Learning and the Bellman Equation<br/><br/>- Deep Q-Networks<br/><br/>- Higher-Level RL Libraries<br/><br/>- DQN Extensions<br/><br/>- Ways to Speed up RL<br/><br/>- Stocks Trading Using RL<br/><br/>- Policy Gradients - an Alternative<br/><br/>- Actor-Critic Methods - A2C and A3C<br/><br/>- The TextWorld Environment<br/><br/>- Web Navigation<br/><br/>- Continuous Action Space<br/><br/>- Trust Regions - PPO, TRPO, ACKTR, and SAC<br/><br/>- Black-Box Optimization in RL<br/><br/>- Advanced Exploration<br/><br/>- RL with Human Feedback<br/><br/> |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you from the basics of RL to more advanced concepts, using various applications, including game playing, discrete optimisation, 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.<br/>The book retains its approach of providing concise, easy-to-follow explanations from 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.<br/>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<br/>*Email sign-up and proof of purchase required<br/>What You Will Learn:<br/>- Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs<br/>- Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG<br/>- Implement RL algorithms using PyTorch and modern RL libraries<br/>- Build and train deep Q-networks to solve complex tasks in Atari environments<br/>- Speed up RL models using algorithmic and engineering approaches<br/>- Leverage advanced techniques like proximal policy optimisation (PPO) for more stable training |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical Term |
Deep reinforcement learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical Term |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical Term |
Reinforcement learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical Term |
Artificial intelligence |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Books and Monographs |
