MARC details
| 000 -LIDER |
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| fixed length control field |
03602nam a22005295i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
978-981-19-4933-3 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250516160144.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240928s2024 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789811949333 |
| -- |
978-981-19-4933-3 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
Q325.5-.7 |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UYQM |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
MAT029000 |
| Source |
bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UYQM |
| Source |
thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Xiao, Zhiqing. |
| Relator term |
author. |
| Authority record control number |
(orcid)0000-0001-5207-638X |
| -- |
https://orcid.org/0000-0001-5207-638X |
| Relator code |
aut |
| -- |
http://id.loc.gov/vocabulary/relators/aut |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Reinforcement Learning |
| Medium |
[electronic resource] : |
| Remainder of title |
Theory and Python Implementation / |
| Statement of responsibility, etc. |
by Zhiqing Xiao. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
1st ed. 2024. |
| 264 #1 - |
|---|
| -- |
Singapore : |
| -- |
Springer Nature Singapore : |
| -- |
Imprint: Springer, |
| -- |
2024. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
XXII, 559 p. 61 illus., 60 illus. in color. |
| Other physical details |
online resource. |
| 336 ## - |
|---|
| -- |
text |
| -- |
txt |
| -- |
rdacontent |
| 337 ## - |
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| -- |
computer |
| -- |
c |
| -- |
rdamedia |
| 338 ## - |
|---|
| -- |
online resource |
| -- |
cr |
| -- |
rdacarrier |
| 347 ## - |
|---|
| -- |
text file |
| -- |
PDF |
| -- |
rda |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Chapter 1. Introduction of Reinforcement Learning (RL) -- Chapter 2. MDP: Markov Decision Process -- Chapter 3. Model-based Numerical Iteration -- Chapter 4. MC: Monte Carlo Learning -- Chapter 5. TD: Temporal Difference Learning -- Chapter 6. Function Approximation -- Chapter 7. PG: Policy Gradient -- Chapter 8. AC: Actor-Critic -- Chapter 9. DPG: Deterministic Policy Gradient -- Chapter 10. Maximum-Entropy RL -- Chapter 11. Policy-based Gradient-Free Algorithms -- Chapter 12. Distributional RL -- Chapter 13. Minimize Regret -- Chapter 14. Tree Search -- Chapter 15. More Agent-Environment Interfaces -- Chapter 16. Learn from Feedback and Imitation Learning. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Reinforcement Learning: Theory and Python Implementation is a tutorial book on reinforcement learning, with explanations of both theory and applications. Starting from a uniform mathematical framework, this book derives the theory of modern reinforcement learning systematically and introduces all mainstream reinforcement learning algorithms such as PPO, SAC, and MuZero. It also covers key technologies of GPT training such as RLHF, IRL, and PbRL. Every chapter is accompanied by high-quality implementations, and all implementations of deep reinforcement learning algorithms are with both TensorFlow and PyTorch. Codes can be found on GitHub along with their results and are runnable on a conventional laptop with either Windows, macOS, or Linux. This book is intended for readers who want to learn reinforcement learning systematically and apply reinforcement learning to practical applications. It is also ideal to academical researchers who seek theoretical foundation or algorithm enhancement in their cutting-edge AI research. |
| 541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Owner |
UABC ; |
| Method of acquisition |
Perpetuidad |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Robotics. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Robotics. |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9789811949326 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9789811949340 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9789811949357 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Public note |
Libro electrónico |
| Uniform Resource Identifier |
http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-19-4933-3 |
| 912 ## - |
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| -- |
ZDB-2-SCS |
| 912 ## - |
|---|
| -- |
ZDB-2-SXCS |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Libro Electrónico |
