Reinforcement learning in network control
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/122414 |
| _version_ | 1826191688407711744 |
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| author | Liu, Bai(Aerospace scientist)Massachusetts Institute of Technology. |
| author2 | Eytan Modiano. |
| author_facet | Eytan Modiano. Liu, Bai(Aerospace scientist)Massachusetts Institute of Technology. |
| author_sort | Liu, Bai(Aerospace scientist)Massachusetts Institute of Technology. |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019 |
| first_indexed | 2024-09-23T08:59:48Z |
| format | Thesis |
| id | mit-1721.1/122414 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:59:48Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1224142023-07-31T13:12:51Z Reinforcement learning in network control Liu, Bai(Aerospace scientist)Massachusetts Institute of Technology. Eytan Modiano. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Aeronautics and Astronautics. Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019 Cataloged from PDF version of thesis. Includes bibliographical references (pages 59-91). With the rapid growth of information technology, network systems have become increasingly complex. In particular, designing network control policies requires knowledge of underlying network dynamics, which are often unknown, and need to be learned. Existing reinforcement learning methods such as Q-Learning, Actor-Critic, etc. are heuristic and do not offer performance guarantees. In contrast, model-based learning methods offer performance guarantees, but can only be applied with bounded state spaces. In the thesis, we propose to use model-based reinforcement learning. By applying Lyapunov analysis, our algorithm can be applied to queueing networks with unbounded state spaces. We prove that under our algorithm, the average queue backlog can get arbitrarily close to the optimal result. We also implement simulations to illustrate the effectiveness of our algorithm. by Bai Liu. S.M. S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics 2019-10-04T21:33:15Z 2019-10-04T21:33:15Z 2019 2019 Thesis https://hdl.handle.net/1721.1/122414 1119730914 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 61 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Aeronautics and Astronautics. Liu, Bai(Aerospace scientist)Massachusetts Institute of Technology. Reinforcement learning in network control |
| title | Reinforcement learning in network control |
| title_full | Reinforcement learning in network control |
| title_fullStr | Reinforcement learning in network control |
| title_full_unstemmed | Reinforcement learning in network control |
| title_short | Reinforcement learning in network control |
| title_sort | reinforcement learning in network control |
| topic | Aeronautics and Astronautics. |
| url | https://hdl.handle.net/1721.1/122414 |
| work_keys_str_mv | AT liubaiaerospacescientistmassachusettsinstituteoftechnology reinforcementlearninginnetworkcontrol |