Intelligent Inventory Control via Ruminative Reinforcement Learning
Inventory management is a sequential decision problem that can be solved with reinforcement learning (RL). Although RL in its conventional form does not require domain knowledge, exploiting such knowledge of problem structure, usually available in inventory management, can be beneficial to improving...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/238357 |
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| _version_ | 1832554281085960192 |
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| author | Tatpong Katanyukul Edwin K. P. Chong |
| author_facet | Tatpong Katanyukul Edwin K. P. Chong |
| author_sort | Tatpong Katanyukul |
| collection | DOAJ |
| description | Inventory management is a sequential decision problem that can be solved with reinforcement learning (RL). Although RL in its conventional form does not require domain knowledge, exploiting such knowledge of problem structure, usually available in inventory management, can be beneficial to improving the learning quality and speed of RL. Ruminative reinforcement
learning (RRL) has been introduced recently based on this approach. RRL is motivated by how humans contemplate the consequences of their actions in trying to learn how to make a better decision. This study further investigates the issues of RRL and proposes new RRL methods applied to inventory management. Our investigation provides insight into different RRL characteristics, and our experimental results show the viability of the new methods. |
| format | Article |
| id | doaj-art-4f4505e743ea48aa8808707b5e2b4ae8 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-4f4505e743ea48aa8808707b5e2b4ae82025-02-03T05:51:53ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/238357238357Intelligent Inventory Control via Ruminative Reinforcement LearningTatpong Katanyukul0Edwin K. P. Chong1Faculty of Engineering, Khon Kaen University, Computer Engineering Building, 123 Moo 16, Mitraparb Road, Muang, Khon Kaen 40002, ThailandDepartment of Electrical and Computer Engineering, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523-1373, USAInventory management is a sequential decision problem that can be solved with reinforcement learning (RL). Although RL in its conventional form does not require domain knowledge, exploiting such knowledge of problem structure, usually available in inventory management, can be beneficial to improving the learning quality and speed of RL. Ruminative reinforcement learning (RRL) has been introduced recently based on this approach. RRL is motivated by how humans contemplate the consequences of their actions in trying to learn how to make a better decision. This study further investigates the issues of RRL and proposes new RRL methods applied to inventory management. Our investigation provides insight into different RRL characteristics, and our experimental results show the viability of the new methods.http://dx.doi.org/10.1155/2014/238357 |
| spellingShingle | Tatpong Katanyukul Edwin K. P. Chong Intelligent Inventory Control via Ruminative Reinforcement Learning Journal of Applied Mathematics |
| title | Intelligent Inventory Control via Ruminative Reinforcement Learning |
| title_full | Intelligent Inventory Control via Ruminative Reinforcement Learning |
| title_fullStr | Intelligent Inventory Control via Ruminative Reinforcement Learning |
| title_full_unstemmed | Intelligent Inventory Control via Ruminative Reinforcement Learning |
| title_short | Intelligent Inventory Control via Ruminative Reinforcement Learning |
| title_sort | intelligent inventory control via ruminative reinforcement learning |
| url | http://dx.doi.org/10.1155/2014/238357 |
| work_keys_str_mv | AT tatpongkatanyukul intelligentinventorycontrolviaruminativereinforcementlearning AT edwinkpchong intelligentinventorycontrolviaruminativereinforcementlearning |