| Presentation | 2022-12-21 Agent based Modeling and Reinforcement Learning for optimal allocation of resources Rashmi Tilak, Toshiharu Sugawara, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We propose a model and notation for business process for delivery of parcels using drones and attempt to improve the total efficiency of the process using reinforcement learning. Although modeling the business processing is one of important applications of multi-agent systems, it is a challenge to design and control the processing efficiently. For this purpose, we train several drones in a way that helps them determine the right number of resources keeping in view the main factors using reinforcement learning. We also examine the use of two types of Q learning algorithms --- temporal difference (TD) and SARSA --- and investigate the difference between the learned behaviors using them, such as utilization percentage of drones, queue size of packages, idle drones. We show that the trained model outperforms the model with no learning involved and SARSA results in the better performance due to their safer learning. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | drones / SARSA / Reinforcement learning / Warehouse delivery |
| Paper # | AI2022-45 |
| Date of Issue | 2022-12-14 (AI) |
| Conference Information | |
| Committee | AI |
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| Conference Date | 2022/12/21(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Yuichi Sei(Univ. of Electro-Comm.) |
| Vice Chair | Yuko Sakurai(AIST) / Tadachika Ozono(Nagoya Inst. of Tech.) |
| Secretary | Yuko Sakurai(Tokyo Univ. of Agriculture and Technology) / Tadachika Ozono(Toho Univ.) |
| Assistant | Kazutaka Matsuzaki(Chuo Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Artificial Intelligence and Knowledge-Based Processing |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Agent based Modeling and Reinforcement Learning for optimal allocation of resources |
| Sub Title (in English) | |
| Keyword(1) | drones |
| Keyword(2) | SARSA |
| Keyword(3) | Reinforcement learning |
| Keyword(4) | Warehouse delivery |
| 1st Author's Name | Rashmi Tilak |
| 1st Author's Affiliation | Waseda University(Waseda University) |
| 2nd Author's Name | Toshiharu Sugawara |
| 2nd Author's Affiliation | Waseda University(Waseda University) |
| Date | 2022-12-21 |
| Paper # | AI2022-45 |
| Volume (vol) | vol.122 |
| Number (no) | AI-322 |
| Page | pp.pp.68-73(AI), |
| #Pages | 6 |
| Date of Issue | 2022-12-14 (AI) |