Advances in Reinforcement Learning
Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different application...
Gorde:
| Formatua: | Online |
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| Hizkuntza: | ingelesa |
| Argitaratua: |
IntechOpen
2021
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| Gaiak: | |
| Sarrera elektronikoa: | ONIX_20210420_9789533073699_264 |
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| _version_ | 1869514091225153536 |
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| collection | Directory of Open Access Books |
| description | Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic. |
| format | Online |
| id | doab-20.500.12854ir-64908 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-649082024-04-14T10:28:17Z Advances in Reinforcement Learning Mellouk, Abdelhamid Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic. 2021-04-20T15:04:44Z 2021-04-20T15:04:44Z 2011 book ONIX_20210420_9789533073699_264 9789533073699 9789535155034 https://directory.doabooks.org/handle/20.500.12854/64908 eng image/jpeg n/a https://www.intechopen.com/books https://mts.intechopen.com/storage/books/24/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/557 10.5772/557 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9789533073699 9789535155034 IntechOpen 484 open access |
| spellingShingle | Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence Advances in Reinforcement Learning |
| title | Advances in Reinforcement Learning |
| title_full | Advances in Reinforcement Learning |
| title_fullStr | Advances in Reinforcement Learning |
| title_full_unstemmed | Advances in Reinforcement Learning |
| title_short | Advances in Reinforcement Learning |
| title_sort | advances in reinforcement learning |
| topic | Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| topic_facet | Machine learning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| url | ONIX_20210420_9789533073699_264 |