Reinforcement learning in the metaverse
In-depth research has been done on AR applications using wireless networks recently to boost user satisfaction, however, neither of the preceding studies took into account how different the edge computing service requirements exists among users, additionally, they do not take sequential scenarios in...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/166698 |
| _version_ | 1826130264200314880 |
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| author | Goyal, Bhavya |
| author2 | Jun Zhao |
| author_facet | Jun Zhao Goyal, Bhavya |
| author_sort | Goyal, Bhavya |
| collection | NTU |
| description | In-depth research has been done on AR applications using wireless networks recently to boost user satisfaction, however, neither of the preceding studies took into account how different the edge computing service requirements exists among users, additionally, they do not take sequential scenarios into account or apply reinforcement learning (RL) strategies to the suggested task hence, the aim of this research is to propose a model which enhances the user satisfaction while socializing in the Metaverse. This research project also aims to compare various pre-existing reinforcement learning algorithms with a novel Quality of Service (QoS) model for AR socialization on a multichannel wireless network. |
| first_indexed | 2024-10-01T07:53:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/166698 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:53:37Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1666982023-05-12T15:36:44Z Reinforcement learning in the metaverse Goyal, Bhavya Jun Zhao School of Computer Science and Engineering junzhao@ntu.edu.sg Engineering::Computer science and engineering In-depth research has been done on AR applications using wireless networks recently to boost user satisfaction, however, neither of the preceding studies took into account how different the edge computing service requirements exists among users, additionally, they do not take sequential scenarios into account or apply reinforcement learning (RL) strategies to the suggested task hence, the aim of this research is to propose a model which enhances the user satisfaction while socializing in the Metaverse. This research project also aims to compare various pre-existing reinforcement learning algorithms with a novel Quality of Service (QoS) model for AR socialization on a multichannel wireless network. Bachelor of Science in Data Science and Artificial Intelligence 2023-05-10T08:02:36Z 2023-05-10T08:02:36Z 2023 Final Year Project (FYP) Goyal, B. (2023). Reinforcement learning in the metaverse. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166698 https://hdl.handle.net/10356/166698 en SCSE22-0538 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Goyal, Bhavya Reinforcement learning in the metaverse |
| title | Reinforcement learning in the metaverse |
| title_full | Reinforcement learning in the metaverse |
| title_fullStr | Reinforcement learning in the metaverse |
| title_full_unstemmed | Reinforcement learning in the metaverse |
| title_short | Reinforcement learning in the metaverse |
| title_sort | reinforcement learning in the metaverse |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/166698 |
| work_keys_str_mv | AT goyalbhavya reinforcementlearninginthemetaverse |