DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Bumjin | ko |
dc.contributor.author | Kang, Cheongwoong | ko |
dc.contributor.author | Choi, Jaesik | ko |
dc.date.accessioned | 2022-04-15T06:48:38Z | - |
dc.date.available | 2022-04-15T06:48:38Z | - |
dc.date.created | 2022-03-14 | - |
dc.date.created | 2022-03-14 | - |
dc.date.created | 2022-03-14 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.citation | APPLIED SCIENCES-BASEL, v.12, no.1 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | http://hdl.handle.net/10203/294805 | - |
dc.description.abstract | This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems. | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.title | Cooperative Multi-Robot Task Allocation with Reinforcement Learning | - |
dc.type | Article | - |
dc.identifier.wosid | 000759185000001 | - |
dc.identifier.scopusid | 2-s2.0-85121973585 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 1 | - |
dc.citation.publicationname | APPLIED SCIENCES-BASEL | - |
dc.identifier.doi | 10.3390/app12010272 | - |
dc.contributor.localauthor | Choi, Jaesik | - |
dc.contributor.nonIdAuthor | Park, Bumjin | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | multi robot task allocation | - |
dc.subject.keywordAuthor | reinforcement learning | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | artificial intelligence | - |
dc.subject.keywordPlus | TAXONOMY | - |
dc.subject.keywordPlus | COORDINATION | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.