Deep Reinforcement Learning (Record no. 1997)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01831nam a22001697a 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789811540974 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| Item number | DON/D |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Dong Hao ed. |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Ding Zihan ed. |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Zhang Shanghang ed. |
| 245 ## - TITLE STATEMENT | |
| Title | Deep Reinforcement Learning |
| Sub Title | : Fundamentals, Research and Applications |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Switzerland |
| Name of publisher | Springer |
| Year of publication | 2020 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | xxvii, 514p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance.<br/>Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations.<br/>The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Machine Learning |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Permanent Location | Current Location | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Full call number | Accession Number | Price effective from | Koha item type | Copy number |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Not For Loan | IIIT Kottayam Central Library | IIIT Kottayam Central Library | Reference | 27/12/2023 | IIITK/07/05/0045/2023-24/LIBRARY BOOKS-II | 14661.48 | 006.31 DON/D | 2061 | 27/12/2023 | Reference | ||||
| IIIT Kottayam Central Library | IIIT Kottayam Central Library | General Stacks | 27/12/2023 | IIITK/07/05/0045/2023-24/LIBRARY BOOKS-II | 14661.48 | 006.31 DON/D | 2062 | 27/12/2023 | Books | 1 | ||||
| IIIT Kottayam Central Library | IIIT Kottayam Central Library | General Stacks | 27/12/2023 | IIITK/07/05/0045/2023-24/LIBRARY BOOKS-II | 14661.48 | 006.31 DON/D | 2063 | 27/12/2023 | Books | 2 |
